{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e472a4d1-521b-446a-9dde-f1a7ee6e45fc",
   "metadata": {},
   "source": [
    "# HDDM WORKSHOP \n",
    "## (Alexander Fengler, May 2022)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b28052e-ccb4-4402-b1e6-70151be73346",
   "metadata": {},
   "source": [
    "## COLAB Installation Instructions (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6ec204a-c56f-485c-a7a4-5ae19463921f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install cython\n",
    "# !pip install pymc # if there are problems --> usually here\n",
    "# !pip install git+https://github.com/hddm-devs/kabuki\n",
    "# !pip install git+https://github.com/hddm-devs/hddm\n",
    "# !pip install torch torchvision torchaudio # optional"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "592a1876-96cc-4c5e-9ecc-2abc632f6f90",
   "metadata": {},
   "source": [
    "## Basic Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cbfe5367-d433-448a-b1e6-f7460acc514a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODULE IMPORTS ----\n",
    "\n",
    "from copy import deepcopy\n",
    "\n",
    "# warning settings\n",
    "import warnings\n",
    "\n",
    "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
    "\n",
    "# Data management\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "# Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "\n",
    "# Stats functionality\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "\n",
    "# HDDM\n",
    "import hddm\n",
    "from hddm.simulators.hddm_dataset_generators import simulator_h_c"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "798d3fc2-50a6-4431-b79e-cd70bcc1ac0f",
   "metadata": {},
   "source": [
    "### Initial Examples (most useful functions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca16f831-0823-4162-a60f-fddce50a2cb3",
   "metadata": {},
   "source": [
    "### Data Simulators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3357a0b-49ca-4bfd-926b-43a8bc74e9be",
   "metadata": {},
   "source": [
    "#### The `simulator_h_c()` function\n",
    "\n",
    "This function is useful if you want to create complex datasets (e.g. for parameter recovery studies). To check specifics you can use the `help()` function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52766dab-62b4-4f3d-add6-8143298f5539",
   "metadata": {},
   "source": [
    "##### Simple example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75e6e279-7052-43bd-8d8c-a781efaa71fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate some data\n",
    "\n",
    "# 'ddm_hddm_base' specifies usage of the basic ddm simulator\n",
    "# recent changes to hddm allow many more models to be fit, necessitating\n",
    "# explicit naming.\n",
    "model = \"ddm_hddm_base\"\n",
    "\n",
    "n_samples = 1000  # number of samples (trials) the simulated data should contain\n",
    "n_subjects = 1\n",
    "\n",
    "data, parameter_dict = simulator_h_c(\n",
    "    n_subjects=n_subjects,\n",
    "    n_trials_per_subject=n_samples,\n",
    "    model=model,\n",
    "    p_outlier=0.00,\n",
    "    conditions=None,\n",
    "    depends_on=None,\n",
    "    regression_models=None,\n",
    "    regression_covariates=None,\n",
    "    group_only_regressors=False,\n",
    "    group_only=None,\n",
    "    fixed_at_default=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e76f6934-9f34-4469-b951-26485b44fb90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>v</th>\n",
       "      <th>a</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.611617</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.539506</td>\n",
       "      <td>2.432656</td>\n",
       "      <td>0.453724</td>\n",
       "      <td>2.203619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.078611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.539506</td>\n",
       "      <td>2.432656</td>\n",
       "      <td>0.453724</td>\n",
       "      <td>2.203619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.529618</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.539506</td>\n",
       "      <td>2.432656</td>\n",
       "      <td>0.453724</td>\n",
       "      <td>2.203619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.592617</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.539506</td>\n",
       "      <td>2.432656</td>\n",
       "      <td>0.453724</td>\n",
       "      <td>2.203619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.960612</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.539506</td>\n",
       "      <td>2.432656</td>\n",
       "      <td>0.453724</td>\n",
       "      <td>2.203619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.770615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.539506</td>\n",
       "      <td>2.432656</td>\n",
       "      <td>0.453724</td>\n",
       "      <td>2.203619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.909613</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.539506</td>\n",
       "      <td>2.432656</td>\n",
       "      <td>0.453724</td>\n",
       "      <td>2.203619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.767615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.539506</td>\n",
       "      <td>2.432656</td>\n",
       "      <td>0.453724</td>\n",
       "      <td>2.203619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.432619</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.539506</td>\n",
       "      <td>2.432656</td>\n",
       "      <td>0.453724</td>\n",
       "      <td>2.203619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.723615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.539506</td>\n",
       "      <td>2.432656</td>\n",
       "      <td>0.453724</td>\n",
       "      <td>2.203619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           rt  response subj_idx         v         a         z         t\n",
       "0    2.611617       1.0        0  2.539506  2.432656  0.453724  2.203619\n",
       "1    3.078611       1.0        0  2.539506  2.432656  0.453724  2.203619\n",
       "2    2.529618       1.0        0  2.539506  2.432656  0.453724  2.203619\n",
       "3    2.592617       1.0        0  2.539506  2.432656  0.453724  2.203619\n",
       "4    2.960612       1.0        0  2.539506  2.432656  0.453724  2.203619\n",
       "..        ...       ...      ...       ...       ...       ...       ...\n",
       "995  2.770615       1.0        0  2.539506  2.432656  0.453724  2.203619\n",
       "996  2.909613       1.0        0  2.539506  2.432656  0.453724  2.203619\n",
       "997  2.767615       1.0        0  2.539506  2.432656  0.453724  2.203619\n",
       "998  2.432619       1.0        0  2.539506  2.432656  0.453724  2.203619\n",
       "999  2.723615       1.0        0  2.539506  2.432656  0.453724  2.203619\n",
       "\n",
       "[1000 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8259d85-4ddd-41bf-ae33-d54d4f2d3bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'t': 2.2036186370169966,\n",
       " 'z': 0.45372422396848033,\n",
       " 'v': 2.5395057155797898,\n",
       " 'a': 2.432655703260449}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating parameters\n",
    "parameter_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c90033-9bb8-452a-b837-e06220eca8ce",
   "metadata": {},
   "source": [
    "##### Complex example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9920da13-27b9-4500-9a6b-5cabe971a5f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate some data\n",
    "\n",
    "# 'ddm_hddm_base' specifies usage of the basic ddm simulator\n",
    "# recent changes to hddm allow many more models to be fit, necessitating\n",
    "# explicit naming.\n",
    "model = \"ddm_hddm_base\"\n",
    "\n",
    "n_samples = 1000  # number of samples (trials) the simulated data should contain\n",
    "n_subjects = 10\n",
    "\n",
    "data, parameter_dict = simulator_h_c(\n",
    "    n_subjects=n_subjects,\n",
    "    n_trials_per_subject=n_samples,\n",
    "    model=model,\n",
    "    p_outlier=0.00,\n",
    "    conditions={\"my_condition\": [\"high\", \"low\"]},\n",
    "    depends_on={\"v\": [\"my_condition\"]},\n",
    "    regression_models=None,\n",
    "    regression_covariates=None,\n",
    "    group_only_regressors=False,\n",
    "    group_only=None,\n",
    "    fixed_at_default=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "741047b5-8b88-4af6-a119-54b0d073ff21",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>my_condition</th>\n",
       "      <th>v</th>\n",
       "      <th>a</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.969172</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>0.871484</td>\n",
       "      <td>2.269515</td>\n",
       "      <td>0.562665</td>\n",
       "      <td>0.749171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.414166</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>0.871484</td>\n",
       "      <td>2.269515</td>\n",
       "      <td>0.562665</td>\n",
       "      <td>0.749171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.240168</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>0.871484</td>\n",
       "      <td>2.269515</td>\n",
       "      <td>0.562665</td>\n",
       "      <td>0.749171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.334167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>0.871484</td>\n",
       "      <td>2.269515</td>\n",
       "      <td>0.562665</td>\n",
       "      <td>0.749171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.184169</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>0.871484</td>\n",
       "      <td>2.269515</td>\n",
       "      <td>0.562665</td>\n",
       "      <td>0.749171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>0.691679</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>low</td>\n",
       "      <td>1.175099</td>\n",
       "      <td>3.082947</td>\n",
       "      <td>0.465346</td>\n",
       "      <td>0.252681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>1.638690</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>low</td>\n",
       "      <td>1.175099</td>\n",
       "      <td>3.082947</td>\n",
       "      <td>0.465346</td>\n",
       "      <td>0.252681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>0.469681</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>low</td>\n",
       "      <td>1.175099</td>\n",
       "      <td>3.082947</td>\n",
       "      <td>0.465346</td>\n",
       "      <td>0.252681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>0.892676</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>low</td>\n",
       "      <td>1.175099</td>\n",
       "      <td>3.082947</td>\n",
       "      <td>0.465346</td>\n",
       "      <td>0.252681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>2.196716</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>low</td>\n",
       "      <td>1.175099</td>\n",
       "      <td>3.082947</td>\n",
       "      <td>0.465346</td>\n",
       "      <td>0.252681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             rt  response subj_idx my_condition         v         a         z  \\\n",
       "0      1.969172       1.0        0         high  0.871484  2.269515  0.562665   \n",
       "1      1.414166       1.0        0         high  0.871484  2.269515  0.562665   \n",
       "2      1.240168       1.0        0         high  0.871484  2.269515  0.562665   \n",
       "3      1.334167       1.0        0         high  0.871484  2.269515  0.562665   \n",
       "4      1.184169       1.0        0         high  0.871484  2.269515  0.562665   \n",
       "...         ...       ...      ...          ...       ...       ...       ...   \n",
       "19995  0.691679       1.0        9          low  1.175099  3.082947  0.465346   \n",
       "19996  1.638690       1.0        9          low  1.175099  3.082947  0.465346   \n",
       "19997  0.469681       1.0        9          low  1.175099  3.082947  0.465346   \n",
       "19998  0.892676       1.0        9          low  1.175099  3.082947  0.465346   \n",
       "19999  2.196716       1.0        9          low  1.175099  3.082947  0.465346   \n",
       "\n",
       "              t  \n",
       "0      0.749171  \n",
       "1      0.749171  \n",
       "2      0.749171  \n",
       "3      0.749171  \n",
       "4      0.749171  \n",
       "...         ...  \n",
       "19995  0.252681  \n",
       "19996  0.252681  \n",
       "19997  0.252681  \n",
       "19998  0.252681  \n",
       "19999  0.252681  \n",
       "\n",
       "[20000 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa756867-9e8d-47a3-966b-936c7951ced2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'z': 0.5643101021992838,\n",
       " 'z_std': 0.06360677869943576,\n",
       " 'a': 2.7837102262781688,\n",
       " 'a_std': 0.3015571727553035,\n",
       " 't': 0.4635284771831927,\n",
       " 't_std': 0.36772697267380816,\n",
       " 'v(high)': 0.739605342236667,\n",
       " 'v(low)': 1.2056254718355417,\n",
       " 'v_std': 0.1656452889266339,\n",
       " 'z_subj.0': 0.5626650388708693,\n",
       " 'a_subj.0': 2.2695148013618454,\n",
       " 't_subj.0': 0.749170924264752,\n",
       " 'v_subj(high).0': 0.8714837960021933,\n",
       " 'z_subj.1': 0.5179780958467182,\n",
       " 'a_subj.1': 2.8261610860934203,\n",
       " 't_subj.1': 0.5663210204665238,\n",
       " 'v_subj(high).1': 0.9168348611044683,\n",
       " 'z_subj.2': 0.5852050980540738,\n",
       " 'a_subj.2': 3.244246589088206,\n",
       " 't_subj.2': 1.0708398724741133,\n",
       " 'v_subj(high).2': 0.6624347419371899,\n",
       " 'z_subj.3': 0.598384195666198,\n",
       " 'a_subj.3': 2.369505820943145,\n",
       " 't_subj.3': 0.05060549118480173,\n",
       " 'v_subj(high).3': 0.6892129036345486,\n",
       " 'z_subj.4': 0.5426574614398145,\n",
       " 'a_subj.4': 2.8428734214486675,\n",
       " 't_subj.4': 0.9275862269918855,\n",
       " 'v_subj(high).4': 0.9580725201560745,\n",
       " 'z_subj.5': 0.6003303459317147,\n",
       " 'a_subj.5': 2.856413385872048,\n",
       " 't_subj.5': 1.0216736847540724,\n",
       " 'v_subj(high).5': 0.5438647994780599,\n",
       " 'z_subj.6': 0.6057129384545266,\n",
       " 'a_subj.6': 2.5977881851590574,\n",
       " 't_subj.6': 0.3081357506105314,\n",
       " 'v_subj(high).6': 0.8207198161728086,\n",
       " 'z_subj.7': 0.5723274107003514,\n",
       " 'a_subj.7': 2.753699629896439,\n",
       " 't_subj.7': 0.34635904888677593,\n",
       " 'v_subj(high).7': 0.7716974672560258,\n",
       " 'z_subj.8': 0.4943641609223215,\n",
       " 'a_subj.8': 2.7577338076192657,\n",
       " 't_subj.8': 0.4633972763828105,\n",
       " 'v_subj(high).8': 0.12080876703328614,\n",
       " 'z_subj.9': 0.46534574935498174,\n",
       " 'a_subj.9': 3.082947324207531,\n",
       " 't_subj.9': 0.2526808238464693,\n",
       " 'v_subj(high).9': 0.6760688054922803,\n",
       " 'v_subj(low).0': 0.8464815789569458,\n",
       " 'v_subj(low).1': 1.115527196696584,\n",
       " 'v_subj(low).2': 1.0225179080129596,\n",
       " 'v_subj(low).3': 1.5310890400297108,\n",
       " 'v_subj(low).4': 1.1664401025341975,\n",
       " 'v_subj(low).5': 1.287542911954266,\n",
       " 'v_subj(low).6': 1.1630218487593167,\n",
       " 'v_subj(low).7': 1.090644053054627,\n",
       " 'v_subj(low).8': 1.136047937725113,\n",
       " 'v_subj(low).9': 1.175098759282437}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating parameters\n",
    "parameter_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f1c1ae9-ba92-43da-9416-a0bf150a68dc",
   "metadata": {},
   "source": [
    "#### The `simulator()` function\n",
    "\n",
    "This function is useful for low level access to the simulators and full flexbility.\n",
    "The `simulator()` function provides a `tuple`, with the following elements as output.\n",
    "\n",
    "- `[0]` provides the *reaction times*\n",
    "- `[1]` provides the *choices*\n",
    "- `[2]` provides a bunch of *metadata*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f378b76-4bf7-44c1-8e70-abdcab121c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get some default parameters\n",
    "\n",
    "# The 'model_config' dictionary contains essential information about all models\n",
    "# currently included in HDDM\n",
    "params = hddm.model_config.model_config[\"ddm_hddm_base\"][\"params_default\"]\n",
    "params[3] = 0.5\n",
    "sim_out = hddm.simulators.simulator(theta=params, model=model, n_samples=n_samples)\n",
    "\n",
    "theta = np.zeros((n_samples, 5))\n",
    "theta[:, 0] = sim_out[2][\"v\"][0]\n",
    "theta[:, 1] = sim_out[2][\"a\"][0]\n",
    "theta[:, 2] = sim_out[2][\"z\"][0]\n",
    "theta[:, 3] = sim_out[2][\"t\"][0]\n",
    "theta[:, 4] = 0\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.concatenate([(sim_out[0]), (sim_out[1]), theta], axis=1),\n",
    "    columns=[\"rt\", \"response\", \"v\", \"a\", \"z\", \"t\", \"subj_idx\"],\n",
    ")\n",
    "data[\"subj_idx\"] = data[\"subj_idx\"].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86d6a439-f65a-4493-9652-39fd5b11254d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'v': array([0.], dtype=float32),\n",
       " 'a': array([2.], dtype=float32),\n",
       " 'z': array([0.5], dtype=float32),\n",
       " 't': array([0.5], dtype=float32),\n",
       " 's': 1.0,\n",
       " 'delta_t': 0.0010000000474974513,\n",
       " 'max_t': 20.0,\n",
       " 'n_samples': 1000,\n",
       " 'simulator': 'ddm',\n",
       " 'boundary_fun_type': 'constant',\n",
       " 'possible_choices': [-1, 1],\n",
       " 'trajectory': array([[   1.       ],\n",
       "        [   1.0387664],\n",
       "        [   1.0491449],\n",
       "        ...,\n",
       "        [-999.       ],\n",
       "        [-999.       ],\n",
       "        [-999.       ]], dtype=float32),\n",
       " 'model': 'ddm_hddm_base'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_out[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "420fff77-bdbd-4ca5-aff4-604010b60e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>v</th>\n",
       "      <th>a</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "      <th>subj_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.038997</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.669000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.694000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.686000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.449991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.810000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.962004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.920998</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.946998</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1.436991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           rt  response    v    a    z    t subj_idx\n",
       "0    1.038997      -1.0  0.0  2.0  0.5  0.5      0.0\n",
       "1    0.669000      -1.0  0.0  2.0  0.5  0.5      0.0\n",
       "2    0.694000      -1.0  0.0  2.0  0.5  0.5      0.0\n",
       "3    0.686000      -1.0  0.0  2.0  0.5  0.5      0.0\n",
       "4    1.449991       1.0  0.0  2.0  0.5  0.5      0.0\n",
       "..        ...       ...  ...  ...  ...  ...      ...\n",
       "995  0.810000      -1.0  0.0  2.0  0.5  0.5      0.0\n",
       "996  2.962004       1.0  0.0  2.0  0.5  0.5      0.0\n",
       "997  0.920998       1.0  0.0  2.0  0.5  0.5      0.0\n",
       "998  0.946998      -1.0  0.0  2.0  0.5  0.5      0.0\n",
       "999  1.436991       1.0  0.0  2.0  0.5  0.5      0.0\n",
       "\n",
       "[1000 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same format as data above\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d91083-d626-42ca-a40f-2a0a4e99bbdb",
   "metadata": {},
   "source": [
    "##### NOTE:\n",
    "\n",
    "In the `hddm.generate` and `hddm.simulators.hddm_dataset_generators` modules you will find yet more options for *simulator* functions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a962ead-027e-4713-a0bb-b23bad41c897",
   "metadata": {},
   "source": [
    "### Plot from Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c173a8d-49f0-4b4e-9b58-8b194e714db2",
   "metadata": {},
   "source": [
    "#### The `plot_from_data()` function\n",
    "\n",
    "This function can be used to illustrate various aspects of a dataset.\n",
    "The example below shows a version that includes a *cartoon of the generative model*,\n",
    "(this can be very useful e.g. in investigating model behavior across parameter settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe15ff2-ecf9-4a3e-9956-9ba84c551e7f",
   "metadata": {},
   "source": [
    "##### SIMPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a982d007-5a63-4183-a28b-9cbc678ebe97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the dataset\n",
    "hddm.plotting.plot_from_data(\n",
    "    df=data,\n",
    "    generative_model=\"ddm_hddm_base\",\n",
    "    columns=1,\n",
    "    groupby=[\"subj_idx\"],\n",
    "    figsize=(4, 3),\n",
    "    value_range=np.arange(0, 6, 0.1),\n",
    "    plot_func=hddm.plotting._plot_func_model,\n",
    "    **{\n",
    "        \"alpha\": 1.0,\n",
    "        \"ylim\": 3,\n",
    "        \"hist_bottom\": 0.0,\n",
    "        \"add_data_rts\": True,\n",
    "        \"add_data_model\": True,\n",
    "        \"add_data_model_markertype_starting_point\": \">\",\n",
    "    }\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20159cb8-3736-4e10-9875-878dbfdbd68d",
   "metadata": {},
   "source": [
    "We can also just show the *reaction times* and drop the *model cartoon*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e655584b-8ca9-4697-bcfd-0053a8340d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the dataset\n",
    "hddm.plotting.plot_from_data(\n",
    "    df=data,\n",
    "    # generative_model = 'ddm_hddm_base',\n",
    "    columns=1,\n",
    "    groupby=[\"subj_idx\"],\n",
    "    figsize=(4, 3),\n",
    "    value_range=np.arange(0, 6, 0.1),\n",
    "    plot_func=hddm.plotting._plot_func_model,\n",
    "    **{\n",
    "        \"alpha\": 1.0,\n",
    "        \"ylim\": 3,\n",
    "        \"add_data_rts\": True,\n",
    "        \"hist_bottom\": 0.0,\n",
    "        \"add_data_model\": False,  # NOTE WE DO NOT SHOW THE MODEL NOW\n",
    "    }\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04298ba-d377-48c6-92e6-cf2262c24360",
   "metadata": {},
   "source": [
    "##### COMPLEX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0eb1cef2-4634-46b7-9a17-b0cd4ac1b681",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate some data\n",
    "\n",
    "# 'ddm_hddm_base' specifies usage of the basic ddm simulator\n",
    "# recent changes to hddm allow many more models to be fit, necessitating\n",
    "# explicit naming.\n",
    "model = \"ddm_hddm_base\"\n",
    "\n",
    "n_samples = 1000  # number of samples (trials) the simulated data should contain\n",
    "n_subjects = 10\n",
    "\n",
    "data, parameter_dict = simulator_h_c(\n",
    "    n_subjects=n_subjects,\n",
    "    n_trials_per_subject=n_samples,\n",
    "    model=model,\n",
    "    p_outlier=0.00,\n",
    "    conditions={\"condition\": [\"high\", \"low\"]},\n",
    "    depends_on={\"v\": [\"condition\"]},\n",
    "    regression_models=None,\n",
    "    regression_covariates=None,\n",
    "    group_only_regressors=False,\n",
    "    group_only=None,\n",
    "    fixed_at_default=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "28042782-f1fc-4916-8b5d-6ccdbe48ae54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>condition</th>\n",
       "      <th>v</th>\n",
       "      <th>a</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.922749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>-1.577771</td>\n",
       "      <td>1.332924</td>\n",
       "      <td>0.673457</td>\n",
       "      <td>1.909757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.142757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>-1.577771</td>\n",
       "      <td>1.332924</td>\n",
       "      <td>0.673457</td>\n",
       "      <td>1.909757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.343755</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>-1.577771</td>\n",
       "      <td>1.332924</td>\n",
       "      <td>0.673457</td>\n",
       "      <td>1.909757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.368755</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>-1.577771</td>\n",
       "      <td>1.332924</td>\n",
       "      <td>0.673457</td>\n",
       "      <td>1.909757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.990757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>-1.577771</td>\n",
       "      <td>1.332924</td>\n",
       "      <td>0.673457</td>\n",
       "      <td>1.909757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>2.692598</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>low</td>\n",
       "      <td>1.637832</td>\n",
       "      <td>2.318110</td>\n",
       "      <td>0.634928</td>\n",
       "      <td>1.527600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>2.482591</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>low</td>\n",
       "      <td>1.637832</td>\n",
       "      <td>2.318110</td>\n",
       "      <td>0.634928</td>\n",
       "      <td>1.527600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>1.980597</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>low</td>\n",
       "      <td>1.637832</td>\n",
       "      <td>2.318110</td>\n",
       "      <td>0.634928</td>\n",
       "      <td>1.527600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>1.700600</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>low</td>\n",
       "      <td>1.637832</td>\n",
       "      <td>2.318110</td>\n",
       "      <td>0.634928</td>\n",
       "      <td>1.527600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>2.019597</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>low</td>\n",
       "      <td>1.637832</td>\n",
       "      <td>2.318110</td>\n",
       "      <td>0.634928</td>\n",
       "      <td>1.527600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             rt  response subj_idx condition         v         a         z  \\\n",
       "0      2.922749       0.0        0      high -1.577771  1.332924  0.673457   \n",
       "1      2.142757       0.0        0      high -1.577771  1.332924  0.673457   \n",
       "2      2.343755       0.0        0      high -1.577771  1.332924  0.673457   \n",
       "3      2.368755       0.0        0      high -1.577771  1.332924  0.673457   \n",
       "4      1.990757       0.0        0      high -1.577771  1.332924  0.673457   \n",
       "...         ...       ...      ...       ...       ...       ...       ...   \n",
       "19995  2.692598       1.0        9       low  1.637832  2.318110  0.634928   \n",
       "19996  2.482591       1.0        9       low  1.637832  2.318110  0.634928   \n",
       "19997  1.980597       1.0        9       low  1.637832  2.318110  0.634928   \n",
       "19998  1.700600       1.0        9       low  1.637832  2.318110  0.634928   \n",
       "19999  2.019597       1.0        9       low  1.637832  2.318110  0.634928   \n",
       "\n",
       "              t  \n",
       "0      1.909757  \n",
       "1      1.909757  \n",
       "2      1.909757  \n",
       "3      1.909757  \n",
       "4      1.909757  \n",
       "...         ...  \n",
       "19995  1.527600  \n",
       "19996  1.527600  \n",
       "19997  1.527600  \n",
       "19998  1.527600  \n",
       "19999  1.527600  \n",
       "\n",
       "[20000 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2fdc2202-1022-43b0-87ef-1b8f7db945f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Nm3Xo0CEVFhaqsLAwaJZ18iJ6yAqywotZsXXrVvXq1Us5OTmaOnWqdu7cqe3bt5f7ZJSsiB6ygqzwYlZ88cUXmjZtmgoLC/X9999r8eLFuvTSS9WqVaugyz9ilRUJUYSQpHvvvVe33nqrxo8fr7Zt2+riiy/Wjh07lJWVpffee0+7d+/WH/7wBw0ZMkR9+vTRtGnTwtr/zJkzlZOTo549e+qCCy7Qtddeq0aNGlW4fY0aNfTYY49pxowZOvbYY3XeeedZbnf//ffrwgsvVEFBgTp37qwNGzbovffeU7169Wy3LS0tTVdffXW174fbvXt3Pfnkk3rkkUfUqVMnLViwQLfcckvY1yYfOHBAb7zxhkaMGGFr+0OHDmnUqFFlJ+UnnniinnjiCUnmdU/PP/+85s+frw4dOujFF1/UxIkTy+2jdevWuuCCC3TOOecoPz9f7du3L9tHdT300EM688wzNWjQIPXt21c9evRQly5dyp7/xz/+ofnz52v27NmqUaOGsrKyNGfOHD3zzDNlt/hp0KCBLrjggnK/ozlz5qhDhw7Kz89Xfn6+OnbsqNmzZwdt8+KLLyo/P1/NmzePyvcD8oK88GZejB8/Xrm5uZowYYJKSkqUm5ur3NzcoE+KyIvoIivICq9lxcKFC7VhwwZ9+OGHatq0qZo0aVK2BCIroousICu8lhWZmZl6/fXX1adPH5144om6+uqr1b59ey1ZskTp6ell28UsK6I6zSVcs337dqNBgwa2ZoENx/Dhw40ePXqE9Zpp06YZ/fr1i2o7quvOO+80unfv7mobVq1aZTRq1ChoRt+qHDhwwMjJyTE++eSTGLYMyYa8qBx5AZjIisqRFYCJrKgcWVFewoyESHaNGzfWs88+q82bN1drP1OnTtXXX3+tDRs26PHHH9dzzz2nK6+8Mqx91KxZs2xCF7cZhqGNGzfqgw8+ULt27VxtS4cOHfTggw9q06ZNtl/z/fffa9y4cUEzFwPVRV5YIy+AYGSFNbICCEZWWCMrKhH1sgY8bejQocbRRx9tZGRkGCeffLIxffr0au+zf//+Ru3atS2XyZMnR6HVFfvll1+MWrVqGd27dy+rzlbUltq1axtLly6NaXuAREJekBeAHWQFWQHYQVYkT1akGEaE028DNv34449BM7QGql+/vurXr+9oezZs2FDhc8cdd1ylE/0AiC3yAoAdZAUAO8iK+BTTIsT06dM1ffr0smEf7dq10/jx4zVgwIBYvSUADyIrANhBVgCwi7wA4ldMixBvv/220tLS1Lp1a0nSc889p4ceekgrV650/boYAPGDrABgB1kBwC7yAohfjl+OUb9+fT300ENh3X8WQPIhKwDYQVYAsIu8AOJDDafe6NChQ/rnP/+pvXv3Ki8vz3Kb0tJSlZaWln19+PBh7d69Ww0aNFBKSopTTQU8zTAM7dmzR8cee6xSU713Axw7WSGRF0B1kRVkBWCH17NC4jwEcIrtvIj1zJerVq0yateubaSlpRk+n8945513Ktx2woQJhiQWyyXXkIz/v3a7LSxeWLZs2RLrwzuqwskKwyAvIl/IEpbghaxI1oUsYAlv8VpWGAbnIZEtZANL9Zeq8iLml2McPHhQmzdv1q+//qrXXntNzzzzjJYsWaKTTz653LahFciioiI1a9ZMW7ZsUXZ2diybGfd+/DFFf/97LY0adVDHHRfTXxk8rri4WDk5Ofr111/l8/ncbo5t4WSFRF5EiiyBH1mR3FlBFsAur2aFxHlIJMgGVIfdvHB8Toi+ffuqVatWmjFjRpXbFhcXy+fzqaioKKkOfqA6EuW4CScrpMT5vgGnJMoxQ1YAsZVIxwznIUBs2T1uHL+wyzCMoCoj7Ckpkf71L3MNJAOyIjbIEiQasiIyZAGSEXlRNbIBTojpxJR33323BgwYoJycHO3Zs0cvvfSSPvroIy1YsCCWb5uQ/vMfqVs3aflyqXNnt1sDRBdZ4RyyBF5GVkQPWYBER15EhmyAE2JahPjpp59UUFCgbdu2yefzqWPHjlqwYIH69esXy7cF4DFkBQA7yAoAdpEXQPyKaRHi2WefjeXuASQIsgKAHWQFALvICyB+efNmvwAAAAAAwHMoQnhEjRpSw4bmGgAiRZYAkMgCANbIBjiB/14e0bGjtHOn260A4HVkCQCJLABgjWyAExgJAQAAAAAAHEERwiP+/W+pdWtzDQCRIksASGQBAGtkA5xAEcIjSkuljRvNNQBEiiwBIJEFAKyRDXACRQgAAAAAAOAIihAAAAAAAMARFCEAAAAAAIAjKEJ4ROvW0oIF5hoAIkWWAJDIAgDWyAY4oYbbDYA92dnS2We73QoAXkeWAJDIAgDWyAY4gZEQHrFtmzRxorkGgEiRJQAksgCANbIBTqAI4RHbtkmTJhEIAKqHLAEgkQUArJENcAJFCAAAAAAA4AiKEAAAAAAAwBEUIQAAAAAAgCMoQnhEvXrSZZeZawCIFFkCQCILAFgjG+AEbtHpES1aSM8/73YrAHgdWQJAIgsAWCMb4ARGQnjEgQPShg3mGgAiRZYAkMgCANbIBjiBIoRHfPON1KaNuQaASJElACSyAIA1sgFOoAgBAAAAAAAcQRECAAAAAAA4giIEAAAAAABwBEUIAAAAAADgCG7R6RGdO0uG4XYrAHgdWQJAIgsAWCMb4ARGQgAAAAAAAEdQhPCI9eulvDxzDQCRIksASGQBAGtkA5xAEcIj9u6Vli0z1wAQKbIEgEQWALBGNsAJFCEAAAAAAIAjYlqEmDJliv7whz+obt26atSokQYPHqz1jO0BEIKsAGAHWQHALvICiF8xLUIsWbJEo0aN0rJly7Ro0SL9/vvvys/P117G9wAIQFYAsIOsAGAXeQHErxTDcO4mLDt37lSjRo20ZMkSnXnmmVVuX1xcLJ/Pp6KiImVnZzvQwvi1e7c0f750zjlS/fputwbxLBGOm3CzQkqM79sJZAn8EuGYISsiRxbArkQ5ZjgPsYdsQHXYPW5qONgmFRUVSZLqV/A/urS0VKWlpWVfFxcXO9IuL6hfX7r8crdbATijqqyQyItIkSVIJGRF5MgCJBvOQ+whG+AExyamNAxDY8aMUY8ePdS+fXvLbaZMmSKfz1e25OTkONW8uLdzp/T3v5trIJHZyQqJvIgUWYJEQVZUD1mAZMJ5iH1kA5zgWBHihhtu0KpVq/Tiiy9WuM1dd92loqKismXLli1ONS/ubdki3XCDuQYSmZ2skMiLSJElSBRkRfWQBUgmnIfYRzbACY5cjnHjjTdq7ty5Wrp0qZo2bVrhdunp6UpPT3eiSQDikN2skMgLIJmRFQDs4jwEiD8xLUIYhqEbb7xRb7zxhj766CO1aNEilm8HwKPICgB2kBUA7CIvgPgV0yLEqFGj9MILL+itt95S3bp1tX37dkmSz+dTZmZmLN8agIeQFQDsICsA2EVeAPErpnNCTJ8+XUVFRerVq5eaNGlStrz88suxfNuEVLeulJ9vroFEQ1Y4hyyBl5EV0UMWINGRF5EhG+CEmF+Ogeho00Z67z23WwHEBlnhHLIEXkZWRA9ZgERHXkSGbIATHLs7Bqrn0CGpuNhcA0CkyBIAElkAwBrZACdQhPCIr7+WfD5zDQCRIksASGQBAGtkA5xAEQIAAAAAADiCIgQAAAAAAHAERQgAAAAAAOAIihAAAAAAAMARMb1FJ6KnQwdpxw7pqKPcbgkALyNLAEhkAQBrZAOcQBHCI2rWlI4+2u1WAPA6sgSARBYAsEY2wAlcjuERGzdKgwaZawCIFFkCQCILAFgjG+AEihAeUVQkvf22uQaASJElACSyAIA1sgFOoAgBAAAAAAAcQRECAAAAAAA4giIEAAAAAABwBEUIjzjuOOnhh801AESKLAEgkQUArJENcAK36PSIxo2lMWPcbgUAryNLAEhkAQBrZAOcwEgIj/jlF+mf/zTXABApsgSARBYAsEY2wAkUITziu++kiy4y1wAQKbIEgEQWALBGNsAJFCEAAAAAAIAjmBMCAAAkmVwVFqaqTh232+GetWuD10BFSkr4zBJAdFGEAAAASWaFevZ0uw3x4fLL3W4B4l8SV+sAxARFCI/IzJRyc801AESKLAEkqbOWLFmqOkk8FOK776Rx46TJk6UWLdxuDeJZSUkJRbskQj8BTqAI4RFt20orVrjdCgBeR5YAkrRSp5xyWNnZbrfDPZ07Sxde6HYr4AXFxYfdbgIcRD8BTuAiLwAAAAAA4AiKEB6xcqWUnm6uASBSZAkAiSwAYI1sgBMoQniEYUgHD5prAIgUWQJAIgsAWCMb4ASKEAAAAAAAwBEUIQAAAAAA5RQUmAsQTdwdAwAAAABQTlGR2y1AIqII4RFt20pr1kgtW7rdEgBeRpYAkMgCANbIBjghppdjLF26VOeee66OPfZYpaSk6M0334zl2yW0zEypXTtzDSQassI5ZAm8jryIDrIAiY6siAzZACfEtAixd+9ederUSdOmTYvl2ySF77+Xhg8310CiISucQ5bA68iL6CALkOjIisiQDXBCTC/HGDBggAYMGBDLt0gaP/8sPfusdP31UvPmbrcGiC6ywjlkCbyOvIgOsgCJjqyIDNkAJ8TVnBClpaUqLS0t+7q4uNjF1gCIZ+RF9RUUmBNO+XzS7NlutwaIDbICgB1kBeCcuLpF55QpU+Tz+cqWnJwct5sEIE6RF9VXVCTNncvM10hsZAUAO8gKwDlxVYS46667VFRUVLZs2bLF7SYBiFPkBQA7yAoAdpAVgHPi6nKM9PR0paenu92MuNS4sXTnneYaAHkRKbIEyYassEYWAMHIChPZACfEVRECFTvuOGnKFLdbAcDryBIAElkAwBrZACfEtAhRUlKiDRs2lH393XffqbCwUPXr11ezZs1i+dYJZ88eaflyqUsXqW5dt1sDRBdZ4ZzALAG8iLyIDvoVSHRkRWTIBjghpnNCfPXVV8rNzVVubq4kacyYMcrNzdX48eNj+bYJ6dtvpd69zTWQaMgK55Al8DryIjrIAiQ6siIyZAOcENOREL169ZJhGLF8CwAJgKwAYBd5AcAOsgKIX8wJ4TH33iulpR352ueTZs92rz0AAAAAANhFEcJjSkqkJUuOfD1okHttAQAAAAAgHDGdEwLRU7OmOVttSorbLQHgZf4sqVnT7ZYAcBNZAMAK2QAnMBLCIzp0kH74gZEPAKrHnyWBfD4zW7i8C0geVlkAAJVlQ0GBVFREfwHVRxECQALJVWFhqurUcbsd3lBUJK1YId1yi/n1LbeYXyM5lJQwGBIAKke/oqjIXK9YIW3eLD36KP0FVMxu38IDRQgOfsm8Tc5NN0k5OcEHvf8kAvBL7hOLFerZ0+02eEuXLpV/jUSW5H9Yk9zq1dKAAdK775qffAKwQr/Cz98/CF0Dwez1LTxQhODgD7RjBycNqEoyn1h01pIlS1Un2auWlVi7Vrr8cun556WnnjI/0fC7915z/ec/u9M2OKukpIS/r0nst9+kH3801wAqknz9Cn8/oXt38458deqYE+P7R0AEroFQdvsWHihCJN/Bb8UfCLm50jPPHHmcEECo5D6xWKlTTjms7Gy32xH/2rY1r+ns3PnIY++8Y84NEfgYEldx8WG3mwAAcS55+xVpaUfuyOfvG/j7DaH9B8DPbt/CA0WI5D34rdStG3zQEwIIxYkFAAAAgHiVzBePAwAAAAAAB1GE8Ig2baTFi6Xatd1uCQAv82dJmzZutwSAm8gCAFY454ATKEJ4RN26Uq9eUg0PXEADIH75s6RuXbdbAsBNZAEAK1bnHD6fOS+Ez+das5BgOKX1iB9/lKZNkw4cCH7c55MKCqTZs91pFwBv8WfJDTdYPx/Y0SBXAG/au7fqbdaulV56yZzg+rjjYt8mAN5gdc5BfwDRxkgIj/jpJ+n++6XS0uDHZ8+WiorcaRMA7/FnyU8/WT8/e7Y0dy65AnhZnTpVL3/4g/Tww9L337vdWgDxpKJzDiCaKEIAAMrxj4goKHC7JQBi6fbbOdkAADiLIkQC4GQBQLQxIgLwrpKSqpdPPjG3/fJL6U9/kn7/3d02AwCSB0WIBOA/WZAoRgAAkOxq1656ycw0t33oIV/eoboAACAASURBVGnePOmKK6RDh9xtNwAgOTAxpUc0aCBdc03l1276J40ZNMiZNgHwHn+WNGgQ3uv8xU0mpwISgz8LBg+WmjaVLrpIysiQnnlGSuUjKiBp2TnnAKqLPzMe0by52THwf3IBAJHwZ0nz5va291/uJXFpBpBIArPgwgul556TZs2SbrpJMgy3WwfALZxzwAmMhPCI/ful//6XoZIAqsefJS1b2ts+cOQDo6yAxBGYBZmZ0uWXm7fkGzHC/PrBB6WUFLdbCcBpnHPACYyE8Ii1a6X27c3JpAAgUv4sWbvW7ZYAcJNVFgwfLj32mDR1qtSpk1l4ZK4pILlwzgEnMBICABLE3r1Vb7N/f+T791+a4fMxNwSQqG680cyJO+6QLr1UuvNOswjBsQ94n51+wi+/2N8f80UhUhQh4oCdQOBabABVqVPH/raRXPPN5LdAchg7Vnr2Wemuu6SsLI59IFGE0084fLjqbTg/QaQoQsSBcALBDj6tBFCVF16o/j4KCswOCFkDJA7/cX3qqeadM0aPNu+ace219C+AZPLDDxU/F5gFFCIQCYoQHpOeXvU2fGIBJCc7129+/bXUo4f0yCPS2WdX7/2KiqS5c8kawItSUqRatcpPPuk/riVzxNS+fdLIkeZklf7+BZdnAN5kt59w5pnS5s3Stm1Skyblt2HSalQXRYg4YCcQBg2Sli2TTjkl9u0B4E21a1e9Tbdu0jnnmCcaq1dLP/0kNW4c+7YBiC+5uVJpaeXbpKRIf/ubedeMYcPMERFDh/JhB+BVdvsJO3eafYO77jJv3QtEG3fHiAO1a9tbxo41Z63etMnefn0+ZrQGUF5qqvTyy+btty69lNtwATAVFJh9h0CpqdKTT0p/+pOZF2+/feQ5/5Bs7qABJJZ69aSTTpKee0764ovKt/XnABmAcFCE8IiSEumNN6S6daW777b3msBPKkKDoaCAsACS0dq10pIl5kiIF1+UPvpIGj++evuk4Al4z9q1UufOwbfoLCqyvrwiLc38NPS886QhQ6RFi8zHZ882L93wX74R2N+ItJ9B/wRwlz8bGjSQOnY054WpbDJrfw5I0ckAJAeKEB5x6JB5jdbIkeaJw5df2ntdRcFQVMREMkAy2r9fKi421717S5MnS3/5izRvXuT7nD2bPAG8Zv9+aeVK+7ftrVHDnNC2b1+zGLF0afDz/v6GPwsi7WdYvc4/BwUnNEDs+bPh8GHzcqxly6Q5c6p+nVUGSBy7sOZIEeKJJ55QixYtlJGRoS5duujjjz924m0T0qBBUrt20m23hXeLvdBiROhwSyAekBXOGzv2SAfhu+/svSZwVuzQxxiaDSeQFe6oVUt67TXzmvGBA82Tk1BW+VBd/skyKXYiXGRF9fTqJV14oXTHHfbmsAsVWpgA/GJehHj55Zd18803a9y4cVq5cqXOOOMMDRgwQJs3b471WyektDTpoYfMTyD8RYVw+MOA2awRb8gKd6Smmtd81q9vDrM+cKDq11jlSOCw7NAOB59iIprICndlZEhvvWVOlN2/v7RiRfDzVvkQbgZUdo25nX2ROZDIimiZOlX6+Wfp/vvtbV/VBxVWl4hzvCafFMMI5/P08J122mnq3Lmzpk+fXvZY27ZtNXjwYE2ZMqXS1xYXF8vn86moqEjZ2dmxbGbc69nTLDwsX27OaJ2fL23ZYs5uX7NmZPv03wvcz3+rrcDHuf2W93j1uKlOVkje/b6dtmKF1KWLmSWdOx95fOVKKS9PuuIK6amnqv8+VjkSmjmBz8F5Xj1myIrosMqCQYPsf8BRXGxemvHf/5rzzLRrV34b/zFfWQaECswEq9dL5r/9ba0sVyrrzwTuq6L2wuTVY4asiIw/G8480zy2Jemee8xixNq1UosW1ds/5x+Jze5xE9NbdB48eFDLly/XnXfeGfR4fn6+Pvvss3Lbl5aWqjTgflHFxcWxbJ6nZGVJr7xiHvgpKWYQ5OaaJwujRkW2z9CDO/C+3/5OiP+xQKFhQXigusLNCom8iFSLFmbnIrQTkZsr/f3v0vDhUvfu0pVXVu99rI5/q8cCc8fqZECyPgEJ3c7/iUskJxCceHgHWRE9LVoc6VeEHkd2ZGdLCxZIZ50l9eljflBywgnB24QeT+EeX5W9PvDT1ooKJ4Hbh/Zl/N9vYP74L/sI99ajdjLEKr+iuf9oSoRMJCsi98ADZj/hmGOOPHbnndLMmdLtt0uvvlq9/Vf0f8pq9JRfZX/jq3NshSMRjot4EtMixK5du3To0CE1DrkJfePGjbV9+/Zy20+ZMkWTJk0KefQxtWlTRykpMWyoB9SoETzEsVMn8yRh4kTzoIhGgTaSkwb/pxChHYGWLY+EV40awZ+4rlgh/f578OP+xwK/X6n8dlbbBr5HuM8lmqFDzYkGvSbcrJDIi+qoUcPMj9A/pNdcI336qTkBbm6uOSt2rAV+4hna4Qgthq5ZY35ttZ2/EzJ3bnD+SOUzRArOoVq1pPbtze89cN+JnBlkBVkRyH+MFBUdOQbsdLTr1zfvlNGz55FCRHU/JbUr3JMAf8Eh8OvQ/PGf6IRua2ffgf2hiraRwi9w2N2/Fav+VkXbSEe2W7MmOBObNEkPv9EuIysi5/9/UKvWkcfq1DGLEwUF5l21evWKbRusPiiVrI8Dq2Mr8P++ZP23vLJzjcB9BG7fubO0eHFwH8OOZDkXCadvEdPLMbZu3arjjjtOn332mfLy8soenzx5smbPnq1169YFbW9VgczJyUm6YVBWfvrJnJn2vffMRZJ++MH81OHmm83Z7eNJ4JDO0Mqh/7nAbUKHgPqDJHC7yiqQkT6XiLw4fDDcrJDIi0j5s+Syy6QRI8p/grh/v3lZxt690ldfxdcktv6TBKthnKHbBX5fgZ+ShDO8O9Ezg6zwzvcdC4FZEHKeJim8SzO2bTOHbh86ZBYimjaNblthsjviInQUWWW/S6u+WOj6nHN+07vv1vLUMUNWRM7qnEMy75bRvbu0b595Mp2W5l4bQ1V2riFZ//+36hsEimYfIFn6FVKcXI7RsGFDpaWllas47tixo1xlUpLS09OVnu69aqsTfvxRuvVW84+8X9Om0pgx0sMPS9ddJ+XkONumqi7H8Kvok4bQfVQk3CGX4TyH+BBuVkjkRaT8WVLRpxiZmebs9126SFddZf47nj4Bsio4SBXnj/81ViO2QvdnJ2vgLrIiegKzwOpHFzgaoKqOc5Mm0gcfmH2UPn3M68jD/aTQy2J9OYaf/3KRyi6V9W8T+lzo7zKSS3C8hKyInD8bzjkn+HKl1FTzlp2nnSY9/bQ5atJNgf+HQ4+NwFFNFQmdXDuWOBcpL6ZFiFq1aqlLly5atGiRzj///LLHFy1apPPOOy+Wb5007rjDDIJ77jFnuHeS1QFfWcc99AD0B4PVa6yKGEhcZEV8adXKzJPBg80i5223ud2iitktHNjJEbIm/pEVzqlsPgUrzZodKUT062cOWW7YMHbtq65oXkduZy6JcG9RWNk8W1VdKhvK6ndpVawI5c/X7OyYzmEfE2RF9f35z+YlBIH/r0891byc8557pIsvlurVc699gech/v/31Z2HBs6JaRFCksaMGaOCggJ17dpVeXl5euqpp7R582aNdLt8liDq1pUmTZKuv968LCM31/k2RHqAM3oBgciK+HLeedLYseZkVKedJp1xhtstskZWJB+ywnmhEzhWpFWrI4WIs882/33UUc61Mxx2iwKVXcIVyuczn69o8lz/e9oZORFYJKiswOBnNwutbp9Y1T6Liw/o5Zft7T+ekBWxMWWKOUpy0iTpr391uzX0A7wq5kWIiy++WD///LPuu+8+bdu2Te3bt9f8+fPVvHnzWL910rjmGnN41G23Se+/H19DpwG7yArnVXViMXmy9Pnn0kUXmbfwdHp4dTJdQwn7yArn+Y8/OyMiTjrJ7Iv06iUNGCAtXGh+YOIlgYUGq1GfFRUwQi/9Cr08InQUQuDP0+q2hYH7jZbK9hVOgcILyIrYaNJEGjdOuvde6X/+R2rbNjr7rerW3vQFEkvMixCSdP311+v666934q0Sls8nnXuuOVlcqJo1pQcfNP9wzJ8vDRwYvfet6F6+QCyQFbHnz5LAY7miE4saNaSXXjJHWP3pT+Ys+DUc+athPRN2onSMUX1kRfUFZkGkKhoV0LGjWXzo08d8j/nzzVuNxwOrkQmhrB63un2gVZ+oslwNPckP/NrO5RGxloj9O7IifHay4ZZbzMvBx4yR3n03Ou9b0eif0CJeos9nkiwc6k6iulq1qvx6wz/+0fzUYexYcwhktE4UQv8oDhrEwQ94mT9L7DrmGOnll6WzzjKvAb3//ti1TbIe/ZCIHWPAbeFmgVR+9FRllzR07WqenOTnS+efb75XPMz5V9GJTuhkjZV9b+GMDLF6XXX3A8SSnWxITzfnjDr/fOmdd6L7AahU8eVOTB6dOChCeMRvv0m//mreHsdKSoo0dar5R3/mTPPWe9VRUaGhsskkAcQ/f5YcdZQ5isqOM880iw+3327evjMWc3oFZg75AsReJFkQ7klzt27m8TxwoHlZ16uv2n+vaLPq14R+P1aXTlSmsssXwrm0gQ91EE/sZsN555mjnW65xZyMtlat2LaLDyQSC0UIj1i92rxlXuAtOkN16WLe7/vee6VLLqneNZgVDQskAABv82fJ8uXmrNd23Xqr9Nln5qzYy5ebn5REA8UHwB2RZoFU/jKCypx1lvTGG+b2l18uzZnj3GVdgWJxuUO0Jtimb4V4YjcbUlLMiSk7dZIef9zsJwB2pbrdAETX5Mlm9XLqVLdbAiCRpKSYo6yOPloaMkTav7/6+wyc94FOOOAds2eHd9z27y+98oo5o/4111Q8qjMS/ksq/HkSLYk2SSMQC+3bS9ddJ913n/TTT263Bl5CEcJj6tSp/I9t8+bS6NFmEWLr1sjeI/B+uwDg5/OZw6nXr5duuCH814eeLBQVUXwAksXgwdLzz5vH/PXXS4YRnf36RzgEjsiIRmEi3EILkKwmTZLS0sx5o8IRqwIivIEihMf8+c/l/9iGuvtuKTPTvCwjEpwYAKhIp07S9OnS//2fuYTD6mQBQGKwc0JxySVmbsyYIZ18cvD20TwhCc0aPlwBYqdBA3MkxLPPSitW2H8dfYLkRhEiAfl80oQJ5tDp1avdbg2ARHPllebkt9dfL61cGf7rGeYMJB67IxKGDZOeeEJat07q0OHI9nZPSCIpVlT14QqZBFTPyJFmYXH06OiNckJiY2JKj+jUyfwjWru2ve3/53+kxx4zZ7NfsMDea7j1JpD4ws2Sijz2mDlp1ZAh5vqoo+y/lpFWgPuilQWVncD7Cwuhd5q47jpp2jTpL3+RTjqp6vcIvXVmRbcsj7QfQyYBR0SSDTVqmJNU9utn3tb7kksq3pbzDUiMhPCMtDQpO9tc21GrlvTgg9J770kLF9p7jf8PO3+MgcQVbpZUJCPDnB9i925zZERlE835P7mkwwHEj2hlQaRzJ7RqZV5ium6d1K5d5fkQeGcLO9vRjwEiF5oNPp+9kUd9+5q37Rw7Vtq3r+LtOE4hUYTwjG+/lc4+21zbNXiw1KOHORri0KHYtQ2Ad0SSJRVp0eLICchDD1W8HR0OIP5EMwus2JmHYdw46a67pG++kfLyqt7n7NnkCBBrodkwe7b9eRsefti8S0ZlfQJAogjhGXv2mCMa9uyx/5qUFPMuGatWSf/4R+zaBsA7IsmSyvzxj+ZJxN13Sx99VP55JoQD4lO0syCUnUmuU1LMW4vffLM0apQ0a1Z028BcD0D4qpMNrVqZx/MDD0ibN9t7DcdpcmJOiAR32mnSRReZt8256KLqX/sJAKHuu0/6/HPzGtAVK6Rjjz3yXOAwagAIlZIiPfKItH+/dM010imn2HudnRMXRk0Azhs3zvzwc+xY6aWXqt6e4zQ5MRIiCUyZIu3caf6RB4Boq1FDeuEF8/rR3FxzdAT3/QZgV0qKeceMyy83C5lvvFH1a0LnovD5+CQViAfZ2ea5x8svS5984nZrEK8oQiSBli2lG280h0Zt3+52awAkosaNpVdeMQueJ53Efb+BZBXp0OrUVOnZZ82RVBdfbP/OXn7MFwHEjyuukLp2NW/Zybx0sEIRwiNycszbWeXkRPb6cePMO2ZMnBjVZgHwmOpmiZWCAnPp3t28T/jDD0vbtkVv/wCiLxZZINm7W0ZFc8XUqGGOphowQDr/fGnx4ui2DUDVopENqanmrbxXrAie64V5ouBHEcIjjj7anLTp6KMje339+ua8EM88I61dG922AfAOqyzxf3IZ6SUURUVHRj60bCkNGSIVFkp9+tDZAOJVdfsV4fBnjH+RKi5S1KtnfnJat67Ur595ly9yBHBOtLIhL0+69FJz4mp/H8HOhLVIDhQhPGL3bun55811pEaNkpo1MyeKCVRQwKy0QLKwyhL/J5fRuoTi2WfNYsTOndKMGdHZJ4Doika/wi5/xviXyk5CZs+W5s2TNm0yCxCrV5tDugE4I5rZ8MADUkmJ9L//W/19IbFQhPCITZvMYsGmTZHvIz1duv9+84/7hx8eedw/ez2VSSDxRSNLqpKdLb32mrRxo3TddZJhxO69AETGiSyojqws6e23zUu88vPN240DiL1oZkPTpuZtvP/2N+nbb6u/PyQOihBJZuhQ87adt90mHT7sdmsAJKr27c1REP/4h/T00263BoAX1a0rvfuudPzxUt++0rp1brcIQLhuvdWccHbMGLdbgnhSw+0GVC1XhYWpqlPH7Xa4yz+Pg39dVGRO9hKJa681h1l9/LH5B746+0L8KSmhtoj4cPnl0qefSjfdJHXpYi4AEI6jjpIWLpR69zbnmVm6VGrVyu1WAbArM9O8HHzePOmss9xuDeKFB4oQK9Szp9ttiB+XX37k39Xt0AfOOs3JQSJJ5oodRcuqhBY0A0VakPTPJbFiRfl9XHmledIwaJBZ/GTumfhBwRJe0bChtGiR1LPnkUJEs2ZutwrJIfn6FVb9hGh8YNmli7R1q7R8uZSSUr19IX7Z7VukGEb8Xq1bXFwsn6+XlixZqjrJdPRb2LTJvL3mxInmsMRbbpEefbR6+4zGPhB/SkpK1LNnXRUVFSk7O9vt5jjGzIvk+X6B6EhJ0qzwJd33HWr9emnYMPP2eSee6HZr7PnhB+nMM6W0NGnJEnOIN2IvWY8Z+hVApKruW3hgJMRKnXLKYSVR5lnq3Fm64IIjX/t85mN2FRSYVUyf78gElOHuA95QXJzMk310pmhZDZEWJm+5xVw/+mjF+9iyxZygik8/4odZsHS7FXDLiSdK//qX260IT9Om0gcfmIWIvn3NQoQTtxhFMqNfIUXWP7DTN0Disdu38EARAtHgvwOG//7cQGKiaFkdkRYm/ZdYdO5c8T4oeMaf5C5YwqtatDhSiOjXz7y0tF49t1uFxEW/Qoqsf2Cnb4DEY7dvwQWhHrFihfkJYnWvx/L5zEJEQUF02gXAW6KVJQC8zctZcMIJ0vvvm5dn9O8vFRe73SIgcXg5G+AdFCGSzOzZ5ogI/0RyAAAAXtO+vXnXjPXrpYEDpb173W4RAMAuihAexYgGAPHEn0nc/QKAUzp3lhYskAoLpfPOkw4ccLtFAAA7mBPCo/yTSzLHA4B44M8kAHDS6adL8+ZJAwZIQ4ZIr78u1arldqsAAJWJ6UiIyZMnq1u3bsrKytJRRx0Vy7cC4GFkhTcUFJiFT//CSCw4jayAlZ49pTfflBYtkv70J+n3391uEdxGVgDxLaZFiIMHD2ro0KG67rrrYvk2SeHkk6VvvzXXQKIhK5xTnSzx32XHvzC3DJxGVkRPovUr8vOlV181s+nKK6VDh9xuEdxEVkQu0bIB8Smml2NMmjRJkjRr1qxYvk1SyMiQWrd2uxVAbJAVzqksS3w+c3SD3UsrmAcCTiMroicR+xXnniu98IJ0ySVSVpY0Y4aUyuxnSYmsiFx1s6GgwPyQwufjwwpUjGj2iO++ky6/3FwDQKQqy5LZs8PrMPjvtsN8EID3JGq/YuhQadYs6dlnpdGjJcNwu0WAt1Q3G/yjJukboDJxVYQoLS1VcXFx0ALTL79Ic+aYawDkRaTIEiQbssJaImdBQYH05JPStGnSHXdQiIA9ZIUpmtnAiElUJOwixMSJE5WSklLp8tVXX0XUmClTpsjn85UtOTk5Ee0HgPtimRUSeQEkCrICsXDttdJf/yo99JB0331utwbRQFZ4DyMmUZGw54S44YYbdMkll1S6zfHHHx9RY+666y6NGTOm7Ovi4mICAPCoWGaFRF7Eiv9Ti8Cv6TwglsgKxMro0dL+/dJdd0mZmdLYsW63CNVBVrgncEQDfQJEQ9hFiIYNG6phw4axaIvS09OVnp4ek30DcFYss0IiL2IltHMRWJAAYoGsQCzdeae0b595WUZmpnTjjW63CJEiK9zj7xvQJ0C0xPTuGJs3b9bu3bu1efNmHTp0SIWFhZKk1q1bq06dOrF864TTpIk0YYK5BhINWeGcSLOkoIBrOuE+siJ6kqlfMWmSOSLippvMQsTw4W63CLFGVkQumbIB7olpEWL8+PF67rnnyr7Ozc2VJC1evFi9evWK5VsnnCZNpIkT3W4FEBtkhXMizRL/bNeAm8iK6EmmfkVKivTgg+aIiGuvNW9BePnlbrcKsURWRC6ZsgHuiendMWbNmiXDMMotHPzhKy6W3nvPXEeDz8enmogfZIVzop0lgJPIiuhJtixISZEef1waNky68krp1VfdbhFiiayIXLJlA9wRV7foRMU2bJD69zfX0TB7NhPLAMko2lkCwJuSMQtSU6Wnn5Yuvlj605+kd95xu0VA/EnGbIDzKEIAAAAgKaSlSc89J517rnThhdL777vdIgBIPhQhAAAAkDRq1pRefFE66yxztv+lS91uEQAkF4oQCaygwFwAAABwRHq69NprUl6eNHCg9PnnbrcIAJIHRQiPSE+XWrUy13YVFZnrQYOYhBKAKdws8fnIECARRdKvSDSZmdJbb0mdOpnXwK9c6XaLAPeRDXBCTG/Riehp167qCWIKCszCg893ZNJJJp8EEMhOlgQiQ4DEFG4WJKo6dcwJKvv2lfLzpY8+Mn82QLIiG+AERkIkkKIiae7cIyMgAAAAUDmfz7wl4bHHmsWIb791u0UAkNgYCeERq1ZJffpIH3wgdezodmsAeBVZAkhSrgoLU1WnjtvtcM+330ojR0pPPim1aeN2a+LDww9LI0ZIZ5whPfOMWZSAVFLCZ5bJJNJ+QuCIbKAqFCE84vffpV27zDUARIosASRphXr2dLsN8eGSS9xuQXw691y3WxBPkrhal4Qi7Sf4R2QDdlCEAAAASaazlixZqjpJPBRi7Vrp8sul55+X2rZ1uzXxpbSUSfkClZSUULQDEFUUIQAAQJJZqVNOOazsbLfb4b62baXOnd1uBeJZcfFht5sAIMFwkVcC4pZ6AAAAAIB4xEgIjzjhBOmzz8x1VbilHoCKhJMlABIXWQDASmXZEPhBJ+cbqA6KEB5Rp46Ul+d2KwB4HVkCQCILAFirLBv8hYdBg5xrDxITl2N4xA8/SGPGmGsAiBRZAkAiCwBYIxvgBIoQHrFjh/Too+Y6kM9n3pcXAOyoKEsAJBeyAICVcLKhoMAcFcFcdAgXl2N43OzZDIkCAAAA4KyiImnuXLdbAS9iJEQC4G4YAAAAAAAvYCREAmB2WgAAAACAFzASwiMaNpSuv95cA0CkyBIAElkAwBrZACcwEsIjmjWT/v53t1sBwOvIEgASWQDAGtkAJzASwiP27ZNWrDDXABApsgSARBYAsGYnG5iPDtVFEcIj1q2TunQx1wAQKbIEgEQWALBmJxtmzzbvisG8dIgURQgAAAAAAOAIihAAAAAAAMARFCEAAAAAAIAjKEJ4RGqqVLeuuQaASJElACSyAIA1sgFO4BadHnHKKVJxsdutAOB1ZAkAiSwAYI1sgBOocQEAAAAAAEfErAixadMmXXPNNWrRooUyMzPVqlUrTZgwQQcPHozVWya0b76R2rUz10AiISucRZbAq8iK6CILkKjIiuohG+CEmF2OsW7dOh0+fFgzZsxQ69attWbNGo0YMUJ79+7V1KlTY/W2CevAATMMDhxwuyVAdJEVziJL4FVkRXSRBUhUZEX1kA1wQsyKEP3791f//v3Lvm7ZsqXWr1+v6dOnEwAAypAVAOwgKwDYQVYA8c/ROSGKiopUv359J98SgAeRFQDsICsA2EFWAPHFsbtjbNy4UY8//rgefvjhCrcpLS1VaWlp2dfFTM0KJB07WSGRF0CyIysA2EFWAPEn7JEQEydOVEpKSqXLV199FfSarVu3qn///ho6dKiGDx9e4b6nTJkin89XtuTk5IT/HSWoli2lt94y14AXxDIrJPIiUmQJ4g1Z4Q6yAF5DVjiDbIATUgzDMMJ5wa5du7Rr165Ktzn++OOVkZEhyTz4e/furdNOO02zZs1SamrFdQ+rCmROTo6KioqUnZ0dTjOBpFVcXCyfz+f6cRPLrJDIC6C6yAqyArCDrCArALvs5kXYl2M0bNhQDRs2tLXtjz/+qN69e6tLly6aOXNmlQd/enq60tPTw21SUti+XZo5U7rqKumYY9xuDVC1WGaFRF5EiixBvCEr3EEWwGvICmeQDXBCzCam3Lp1q3r16qWcnBxNnTpVO3fu1Pbt27V9+/ZYvWVC27pVuvtucw0kErLCWWQJvIqsiC6yAImKrKgesgFOiNnElAsXLtSGDRu0YcMGNW3aNOi5MK8AAZDAyAoAdpAVAOwgK4D4F7OREMOGDZNhGJYLAPiRFQDsICsA2EFWAPEvZkUIAAAAAACAQBQhPOKoo6QhQ8w1AESKLAEgkQUArJENcELM5oRAdLVsKf3zfvqfbQAAIABJREFUn263AoDXkSUAJLIAgDWyAU5gJIRHHDwo/fCDuQaASJElACSyAIA1sgFOoAjhEWvWSDk55hoAIkWWAJDIAgDWyAY4gSIEAAAAAABwBEUIAAAAAADgCIoQAAAAAADAERQhAAAAAACAI7hFp0eccop04IBUs6bbLQHgZWQJAIksAGCNbIATKEJ4RGqqlJ7udisAeB1ZAkAiCwBYIxvgBC7H8Ij//Efq1ctcA0CkyBIAElkAwBrZACdQhPCIkhJpyRJzDQCRIksASGQBAGtkA5xAEQIAAAAAADiCIgQAAAAAAHAERQgAAAAAAOAIihAe0ayZ9PTT5hoAIkWWAJDIAgDWyAY4gVt0ekTDhtLw4W63AoDXkSUAJLIAgDWyAU5gJIRH7NolPfOMuQaASJElACSyAIA1sgFOoAjhEZs3SyNGmGsAiBRZAkAiCwBYIxvgBIoQAAAAAADAERQhAAAAAACAIyhCAAAAAAAAR1CE8Ig6daSePc01AESKLAEgkQUArJENcAK36PSIE06QPvrI7VYA8DqyBIBEFgCwRjbACYyE8IjDh6XSUnMNAJEiSwBIZAEAa2QDnEARwiMKC6WMDHMNAJEiSwBIZAEAa2QDnEARAgAAAAAAOIIiBAAAAAAAcERMixCDBg1Ss2bNlJGRoSZNmqigoEBbt26N5VsC8CCyAoAdZAUAO8gKIL7FtAjRu3dvvfLKK1q/fr1ee+01bdy4UUOGDInlWwLwILICgB1kBQA7yAogvqUYhmE49WZz587V4MGDVVpaqpo1a1a5fXFxsXw+n4qKipSdne1AC+PXwYPSjh1So0ZSrVputwbxLBGOm3CzQkqM79sJZAn8EuGYISsiRxbArkQ4ZsgK+8gGVIfd46aGUw3avXu35syZo27dutk++HFErVpS06ZutwKIPbIitsgSJAqyonrIAiQLsiI8ZAOcEPMixB133KFp06Zp3759Ov300zVv3rwKty0tLVVpaWnZ10VFRZLMikqy++67FE2YkKFJkw6oRQvHBq/Ag/zHi4ODnKIinKyQyItIkSXwIyuSOyvIAthFViRXVpANqA7beWGEacKECYakSpcvv/yybPudO3ca69evNxYuXGh0797dOOecc4zDhw9HvG8WFhZ7y5YtW8I9vKMqlllhd/8sLCxVL2QFCwuLnYWsYGFhsbtUlRdhzwmxa9cu7dq1q9Jtjj/+eGVkZJR7/IcfflBOTo4+++wz5eXllXs+tAL566+/qnnz5tq8ebN8Pl84zUxoxcXFysnJ0ZYtW5LqGrWq8HMxGYahPXv26Nhjj1Vqqnt34Y1lVkjkhR0cE9b4uZjICrIiEMdFefxMTGQFWRGI48IaPxeT3bwI+3KMhg0bqmHDhhE3SlLQAR4oPT1d6enp5R73+XxJ/cusSHZ2Nj8XC/xcFBd/LGOZFRJ5EQ6OCWv8XMiKZP/9W+G4KI+fCVmR7L9/KxwX1vi52MuLmM0J8cUXX+iLL75Qjx49VK9ePf33v//V+PHj1apVqworkACSD1kBwA6yAoAdZAUQ/2I2piozM1Ovv/66+vTpoxNPPFFXX3212rdvryVLllhWGQEkJ7ICgB1kBQA7yAog/sVsJESHDh304YcfVmsf6enpmjBhAoERgp+LNX4u3hSNrJD4/VvhZ2KNn4s3kRWxxc+lPH4m3kRWxBY/F2v8XMIT9sSUAAAAAAAAkXBvilsAAAAAAJBUKEIAAAAAAABHUIQAAAAAAACOiOsixBNPPKEWLVooIyNDXbp00ccff+x2k1wzZcoU/eEPf1DdunXVqFEjDR48WOvXr3e7WXFnypQpSklJ0c033+x2U+AgsiIYeVE1siI5kRXByIqqkRXJi7w4gqywh7ywL26LEC+//LJuvvlmjRs3TitXrtQZZ5yhAQMGaPPmzW43zRVLlizRqFGjtGzZMi1atEi///678vPztXfvXrebFje+/PJLPfXUU+rYsaPbTYGDyIryyIvKkRXJiawoj6yoHFmRvMiLYGRF1ciLMBlx6tRTTzVGjhwZ9NhJJ51k3HnnnS61KL7s2LHDkGQsWbLE7abEhT179hht2rQxFi1aZPTs2dMYPXq0202CQ8iKqpEXR5AVyYusqBpZcQRZkdzIi8qRFcHIi/DF5UiIgwcPavny5crPzw96PD8/X5999plLrYovRUVFkqT69eu73JL4MGrUKA0cOFB9+/Z1uylwEFlhD3lxBFmRnMgKe8iKI8iK5EVeVI2sCEZehK+G2w2wsmvXLh06dEiNGzcOerxx48bavn27S62KH4ZhaMyYMerRo4fat2/vdnNc99JLL2nFihX68ssv3W4KHEZWVI28OIKsSF5kRdXIiiPIiuRGXlSOrAhGXkQmLosQfikpKUFfG4ZR7rFkdMMNN2jVqlX65JNP3G6K67Zs2aLRo0dr4cKFysjIcLs5cAlZUTHywkRWQCIrKkNWmMgK+JEX1siKI8iLyMVlEaJhw4ZKS0srV23csWNHuapksrnxxhs1d+5cLV26VE2bNnW7Oa5bvny5duzYoS5dupQ9dujQIS1dulTTpk1TaWmp0tLSXGwhYomsqBx5cQRZkdzIisqRFUeQFSAvKkZWBCMvIheXc0LUqlVLXbp00aJFi4IeX7Rokbp16+ZSq9xlGIZuuOEGvf766/rwww/VokULt5sUF/r06aPVq1ersLCwbOnatasuu+wyFRYWcuAnOLLCGnlRHlmR3MgKa2RFeWQFyIvyyApr5EXk4nIkhCSNGTNGBQUF6tq1q/Ly8vTUU09p8+bNGjlypNtNc8WoUaP0wgsv6K233lLdunXLqrM+n0+ZmZkut849devWLXc9Wu3atdWgQQOuU0sSZEV55EV5ZAXIivLIivLICkjkRSiywhp5Ebm4LUJcfPHF+vnnn3Xfffdp27Ztat++vebPn6/mzZu73TRXTJ8+XZLUq1evoMdnzpypYcOGOd8gIE6QFeWRF0B5ZEV5ZAVgjbwIRlYg2lIMwzDcbgQAAAAAAEh8cTknBAAAAAAASDwUIQAAAAAAgCMoQgAAAAAAAEdQhAAAAAAAAI6gCAEAAAAAABxBEQIAAAAAADiCIgQAAAAAAHAERQgAAAAAAOAIihAAAAAAAMARFCEAAAAAAIAjKEIAAAAAAABHUIQAAAAAAACOoAgBAAAAAAAcQRECAAAAAAA4giIEAAAAAABwBEUIAAAAAADgCIoQAAAAAADAERQhAAAAAACAIyhCAAAAAAAAR1CEAAAAAAAAjqAIAQAAAAAAHEERAgAAAAAAOIIiBAAAAAAAcARFCAAAAAAA4AiKEAAAAAAAwBEUIQAAAAAAgCMoQgAAgP/H3p2GR1Hlbx+/E5YkbC0kgihhExcQZHUBVHBDlhFXxBmMgoKiiCiKigugDn90UFBkZNRBYFAUNxQVEZTNjVEIYRmRGRAEWURAE4IaBOp5UU+F7qSTVCddVV3d38919VWku7r6JKHunPrVqVMAAACuoAgBAAAAAABcQRECAAAAAAC4giIEAAAAAABwBUUIAAAAAADgCooQAAAAAADAFRQhAAAAAACAKyhCAAAAAAAAV1CEAAAAAAAArqAIAQAAAAAAXEERAgAAAAAAuIIiBAAAAAAAcAVFCAAAAAAA4AqKEAAAAAAAwBUUIQAAAAAAgCsoQgAAAAAAAFdQhAAAAAAAAK6gCAEAAAAAAFxBEQIAAAAAALiCIgQAAAAAAHAFRQgAAAAAAOAKihAAAAAAAMAVFCEAAAAAAIArKEIAAAAAAABXUIQAAAAAAACuoAgBAAAAAABcQRECAAAAAAC4giIEAAAAAABwBUUIAAAAAADgCooQFdC4cWM9/fTThV8nJSXpnXfeKfU9/fv31+WXX+5Ie8477zzNmjXLdnuWLFmipKQk/fLLL7Y/Y8yYMWrTpk2p61x99dWaMGGC7W1WlJ022VnHYud31LVrV91555222yhJBw8eVLNmzfT555/bfs/atWvVoEEDHThwIKLPQuwhL8IjL8IjLxIXWREeWREeWZG4yIrwyIrwYi0rKEJE0c6dO9WjRw9J0pYtW5SUlKScnJyQdZ555hlNnz496p/9/vvva9euXbr22mttv6dTp07auXOnAoFAVNsyatQojR07Vnl5eVHdbkXcc889+uSTT2yt69Tv6IUXXlCjRo3UuXPnYq8VFBSoTZs2xf7PtGrVSmeeeaYmTpwY9fbAW+SFibwIL1xeNG7cWElJSSGP+++/v/B18iI+kRUmsiK8kvoWH3zwgc466yylpaUpIyNDV155ZeFrZEV8IitMZEV4RbPCKgKFe3z99deSnM0KihBRdNxxxyklJaXUdQKBgI455piof/akSZM0YMAAJSfb/5VWrVpVxx13nJKSkqLaltNPP12NGzfWK6+8EtXtVkSNGjWUnp5ua12nfkfPPvusBg4cGPa1e++9V8cff3zY1wYMGKApU6bo8OHDUW8TvENemMiL8ErKi0cffVQ7d+4sfDz00EMhr5MX8YesMJEV4YXLirfeektZWVkaMGCAVq9erc8//1x/+ctfQtYhK+IPWWEiK8IrmhVWESj4MXDgQDVu3FgdOnQoXM+prIibIsSRI0f0xBNPqFmzZkpJSVHDhg01duxYSeZQkgsuuEBpaWlKT0/XzTffrPz8/ML3WsNennzySdWvX1/p6ekaMmSI/vjjj8J1du/erUsvvVRpaWlq0qRJ2P/YwcOOmjRpIklq27atkpKS1LVr15DPshQUFOiOO+5Q3bp1lZqaqnPOOaew+iQdrVJ98skn6tChg6pVq6ZOnTppw4YNhevs2bNHH3/8sXr37l2sTXv27NEVV1yhatWq6aSTTtLcuXOLbTt4GNSLL76ozMxMVatWTVdccYUmTJgQdkeYOXOmGjdurEAgoGuvvVb79+8Peb1379569dVXi70vnO+//16XXnqpateurerVq+u0007TvHnzJEnTp08v9vnvvPNO2MB6/vnnC9vep0+fkO+rIsOgDhw4oOuvv141atRQ/fr19dRTT4Ws/+2336patWohQ9Defvttpaamau3atZKk7Oxsbdy4Ub169Sr2eR9++KEWLFigJ598Mmx7LrnkEu3du1dLly611X6UjbwgL/yaFzVr1tRxxx1X+KhRo0bI6+RFdJEVZIXfsuLQoUMaNmyYxo8fr8GDB+vkk0/WKaecoquvvjpk+2RFdJEVZIXfssIqAlmP9PR0zZ07VzfeeGPI9+dUVsRNEWLkyJF64okn9PDDD+ubb77RrFmzVK9ePf3666/q3r27ateura+//lpvvPGGPv74Y91+++0h71+8eLE2bdqkxYsXa8aMGZo+fXrIUJj+/ftry5YtWrRokd58800999xz2r17d4nt+eqrryRJH3/8sXbu3Km333477Hr33nuv3nrrLc2YMUPZ2dlq1qyZLrnkEu3bty9kvQcffFBPPfWUVqxYocqVK+vGG28sfO2zzz5TtWrV1Lx582Lbf+SRR3TNNddozZo16tmzp/r161ds25bPP/9cgwcP1rBhw5STk6OLL764MECDbdq0Se+8847ef/99vf/++1q6dKkef/zxkHXOPPNMffXVVyooKCjxZ2QZMmSICgoKtGzZMq1du1ZPPPFEsY51WTZu3KjXX39d7733nubPn6+cnBwNGTIkom2UZMSIEVq8eLHmzJmjBQsWaMmSJVq5cmXh66eeeqqefPJJ3Xbbbfr++++1Y8cODRo0SI8//rhatWolSVq2bJlOPvlk1apVK2TbP/74owYNGqSZM2eqWrVqYT+/atWqat26tT799NOofD8gL8gLf+aFJD3xxBNKT09XmzZtNHbsWB08eDDkdfIiusgKssJvWZGdna3t27crOTlZbdu2Vf369dWjRw/95z//Cfl8siK6yAqywm9ZUdTcuXO1Z88e9e/fP+R5x7LCiAN5eXlGSkqK8eKLLxZ77YUXXjBq165t5OfnFz73wQcfGMnJycauXbsMwzCMG264wWjUqJFx6NChwnX69Olj9O3b1zAMw9iwYYMhyVi+fHnh6+vXrzckGRMnTix8TpIxZ84cwzAMY/PmzYYkY9WqVSHtueGGG4zLLrvMMAzDyM/PN6pUqWK88sorha8fPHjQOP74442//e1vhmEYxuLFiw1JxscffxzSfknGb7/9ZhiGYUycONFo2rRpse9dkvHQQw8Vfp2fn28kJSUZH374Yci2f/75Z8MwDKNv375Gr169QrbRr18/IxAIFH49evRoo1q1akZeXl7hcyNGjDDOOuuskPetXr3akGRs2bKlWLuKatWqlTFmzJiwr02bNi3k8w3DMObMmWME/9cdPXq0UalSJWPbtm2Fz3344YdGcnKysXPnzsJ1WrduXWZbDCP0d7R//36jatWqxmuvvVb4+t69e420tDRj2LBhIe/r1auXce655xoXXnihcfHFFxtHjhwpfG3YsGHGBRdcELL+kSNHjO7duxuPPfaYYRgl/58xDMO44oorjP79+9tqP0pHXpAXfswLwzCMCRMmGEuWLDFWr15tvPjii0ZGRoZx0003FVuPvIgOsoKs8GNWvPrqq4Yko2HDhsabb75prFixwvjzn/9spKenG3v37g1Zl6yIDrKCrPBjVhTVo0cPo0ePHmFfcyIr4mIkxPr161VQUKALL7ww7GutW7dW9erVC5/r3Lmzjhw5EjKU6LTTTlOlSpUKv65fv35hhXH9+vWqXLlyyPUxp556aoWv19m0aZP++OOPkMmEqlSpojPPPFPr168PWff0008PaZukwvb99ttvSk1NDfsZwe+rXr26atasWWLldMOGDTrzzDNDniv6tWROjlazZs2Q9hTdZlpamiTp119/DftZwe644w799a9/VefOnTV69GitWbOmzPcU1bBhQzVo0KDw644dOxb7HZfHpk2bdPDgQXXs2LHwuTp16uiUU04ptu5LL72kNWvWKDs7W9OnTw8ZyhTud/Tss88qLy9PI0eOLLMdaWlptn6WKBt5QV74MS8k6a677lKXLl10+umna+DAgfrHP/6hqVOnau/evSHrkRfRQVaQFX7MiiNHjkgyz1xfddVVat++vaZNm6akpCS98cYbIeuSFdFBVpAVfsyKYD/88IM++ugj3XTTTWFfdyIr4qIIYf1HC8cwjBInPAl+vkqVKsVes4LcMIxi60dDSdsN1+bg9lmvWe3LyMjQzz//HPYzSvu+wrUnXFvKs01rqNWxxx4b9rOCDRw4UN99952ysrK0du1adejQQc8++6wkKTk5uVgbgq+RK4n1fVT0dxbu+y/J6tWrdeDAAR04cEC7du0KeS3c72jRokVavny5UlJSVLlyZTVr1kyS1KFDB91www0h6+7bt8/WzxJlIy/Ii6L8kBfhnH322ZLMYaDByIvoICvIiqL8kBXWAWKLFi0Kn0tJSVHTpk21devWkHXJiuggK8iKovyQFcGmTZum9PT0sPN6SM5kRVwUIU466SSlpaWFvfVJixYtlJOTE3J/088//1zJyck6+eSTbW2/efPmOnTokFasWFH43IYNG0q9r23VqlUlqdSZRJs1a6aqVavqs88+K3zujz/+0IoVK8JeV1WStm3bateuXbY6raU59dRTC68hswR/z5FYt26dGjRooIyMDFvrZ2ZmavDgwXr77bd1991368UXX5Rkhsf+/ftDfn9FbzckSVu3btWOHTsKv/7yyy8j+h2XpFmzZqpSpYqWL19e+NzPP/+s//73vyHr7du3T/3799eDDz6oAQMGqF+/fvrtt98KX2/btq2+/fbbkDCZNGmSVq9erZycHOXk5BROgjN79uxi18CtW7dObdu2rdD3AhN5QV74MS/CWbVqlaSjBx0W8iI6yAqywo9Z0b59e6WkpIScgf3jjz+0ZcsWNWrUKGT7ZEV0kBVkhR+zwmIYhqZNm6brr7++WIHH4kRWxEURIjU1Vffdd5/uvfde/etf/9KmTZu0fPlyTZ06Vf369VNqaqpuuOEGrVu3TosXL9bQoUOVlZWlevXq2dr+Kaecou7du2vQoEH697//rZUrV2rgwIGlVj7r1q2rtLQ0zZ8/Xz/++KNyc3OLrVO9enXdeuutGjFihObPn69vvvlGgwYN0q+//lricJhw2rZtq2OPPVaff/657feEM3ToUM2bN08TJkzQ//73Pz3//PP68MMPy1XF+/TTT9WtWzdb695555366KOPtHnzZmVnZ2vRokWF4XfWWWepWrVqeuCBB7Rx40bNmjUr7L1zrd/x6tWr9emnn+qOO+7QNddco+OOOy7itgerUaOGbrrpJo0YMUKffPKJ1q1bp/79+xe7BdHgwYOVmZmphx56SBMmTJBhGLrnnnsKXz///PN14MCBkImhGjZsqJYtWxY+rKA68cQTQ4Z0bdmyRdu3b9dFF11Uoe8FJvKCvPBjXnz55ZeaOHGicnJytHnzZr3++uu65ZZb1Lt3bzVs2LBwPfIiesgKssKPWVGrVi0NHjxYo0eP1oIFC7RhwwbdeuutkqQ+ffoUrkdWRA9ZQVb4MSssixYt0ubNm0v8nTuVFXFRhJCkhx9+WHfffbdGjRql5s2bq2/fvtq9e7eqVaumjz76SPv27dMZZ5yhq6++WhdeeKEmT54c0fanTZumzMxMdenSRVdeeaVuvvlm1a1bt8T1K1eurEmTJun555/X8ccfr8suuyzseo8//riuuuoqZWVlqV27dtq4caM++ugj1a5d23bbKlWqpBtvvLHC98Pt3Lmz/vGPf2jChAlq3bq15s+fr7vuuqvUa4jC+f333zVnzhwNGjTI1vqHDx/WkCFD1Lx5c3Xv3l2nnHKKnnvuOUnmdU8vv/yy5s2bp1atWunVV1/VmDFjim2jWbNmuvLKK9WzZ09169ZNLVu2LNxGRY0fP17nnXeeevfurYsuukjnnHOO2rdvX/j6v/71L82bN08zZ85U5cqVVa1aNb3yyiv65z//WTi6IT09XVdeeWW5fkevvvqqunXrVuwMBsqPvCAv/JYXKSkpmj17trp27aoWLVpo1KhRGjRoULFbkJEX0UVWkBV+ywpr29dee62ysrJ0xhln6Pvvv9eiRYtCfv9kRXSRFWSFH7NCkqZOnapOnTqVOPrFsayI6jSX8MyuXbuM9PR0W7PARmLgwIHGOeecE9F7Jk+ebFx88cVRbUdF3X///Ubnzp09bcOaNWuMunXrhszoW5bff//dyMzMND777DMHW4ZEQ16UjrwATGRF6cgKwERWlI6sKC5uRkIkunr16mnq1KnFJh2K1JNPPqnVq1dr48aNevbZZzVjxoxikySWpUqVKoUTunjNMAxt2rRJn3zyiU477TRP29KqVSv97W9/05YtW2y/5/vvv9eDDz4YMnMxUFHkRXjkBRCKrAiPrABCkRXhkRWliHpZA77Wp08f49hjjzVSU1ONFi1aGFOmTKnwNrt3725Ur1497GPs2LFRaHXJfv75Z6Nq1apG586dC6uzJbWlevXqxrJlyxxtDxBPyAvyArCDrCArADvIisTJiiTDiODeH0A5bN++PWSG1mB16tRRnTp1XG1P0dvZBTvhhBNKnegHgLPICwB2kBUA7CArYpOjRYgpU6ZoypQphcM+TjvtNI0aNUo9evRw6iMB+BBZAcAOsgKAXeQFELscLUK89957qlSpkpo1ayZJmjFjhsaPH69Vq1Z5fl0MgNhBVgCwg6wAYBd5AcQu1y/HqFOnjsaPHx/R/WcBJB6yAoAdZAUAu8gLIDZUduuDDh8+rDfeeEMHDhxQx44dw65TUFCggoKCwq+PHDmiffv2KT09XUlJSW41FfA1wzC0f/9+HX/88UpO9t8NcOxkhUReABVFVpAVgB1+zwqJ4xDALbbzwumZL9esWWNUr17dqFSpkhEIBIwPPvigxHVHjx5tSOJh69HWkIz/v/S6LTxi8bFt2zand++oiiQrDIO8qPiDDOFhPsgKHuQBDzsPv2WFYXAcYv9BBvCI7qOsvHD8coyDBw9q69at+uWXX/TWW2/pn//8p5YuXaoWLVoUW7doBTI3N1cNGzbUtm3bVKtWLSeb6Tvbtyfp73+vqiFDDuqEExz9FcJn8vLylJmZqV9++UWBQMDr5tgWSVZI5EVFkSEgK8gKC3mA0vg1KySOQ+wiAxAtdvPC9TkhLrroIp144ol6/vnny1w3Ly9PgUBAubm5cb/zA9ESL/tNJFkhxc/3DbglXvYZsgJwVjztMxyHAM6yu9+4fmGXYRghVUaUT36+9OWX5hKIR2SFs8gQxAuyouLIAyQK8iI8MgBuc3RiygceeEA9evRQZmam9u/fr9dee01LlizR/PnznfzYhPDf/0qdOkkrV0rt2nndGqBiyAr3kSHwI7LCGeQB4hF5YR8ZALc5WoT48ccflZWVpZ07dyoQCOj000/X/PnzdfHFFzv5sQB8hqwAYAdZAcAu8gKIXY4WIaZOnerk5gHECbICgB1kBQC7yAsgdvnzZr8AAAAAAMB3KEL4VOXKUkaGuQSASJEhACzkAZDYyAC4jf9qPnX66dJPP3ndCgB+RYYAsJAHQGIjA+A2RkIAAAAAAABXUITwqf/8R2rWzFwCQKTIEAAW8gBIbGQA3EYRwqcKCqRNm8wlAESKDAFgIQ+AxEYGwG0UIQAAAAAAgCsoQgAAAAAAAFdQhAAAAAAAAK6gCOFTzZpJ8+ebSwCIFBkCwEIeAImNDIDbKnvdAJRPrVrSJZd43QoAfkWGALCQB0BiIwPgNkZC+NTOndKYMeYSACJFhgCwkAdAYiMD4DaKED61c6f0yCOEBYDyIUMAWMgDILGRAXAbRQgAAAAAAOAKihAAAAAAAMAVFCEAAAAAAIArKEL4VO3aUr9+5hIAIkWGALCQB0BiIwPgNm7R6VNNmkgvv+x1KwD4FRkCwEIeAImNDIDbGAnhU7//Lm3caC4K8lg3AAAgAElEQVQBIFJkCAALeQAkNjIAbqMI4VPffCOddJK5BIBIkSEALOQBkNjIALiNIgQAAAAAAHAFRQgAAAAAAOAKihAAAAAAAMAVFCEAAAAAAIAruEWnT7VrJxmG160A4FdkCAALeQAkNjIAbmMkBAAAAAAAcAVFCJ/asEHq2NFcAkCkyBAAFvIASGxkANxGEcKnDhyQli83lwAQKTIEgIU8ABIbGQC3UYQAAAAAAACucLQIMW7cOJ1xxhmqWbOm6tatq8svv1wbGOcDoAiyAoAdZAUAu8gLIHY5WoRYunSphgwZouXLl2vhwoU6dOiQunXrpgOM9QEQhKwAYAdZAcAu8gKIXUmG4d4NWX766SfVrVtXS5cu1XnnnVfm+nl5eQoEAsrNzVWtWrVcaKF/7NsnzZsn9ewp1anjdWsQS+Jhv4k0K6T4+L7dRIYgHvYZsiI6yAOUJl72GY5DSkYGIFrs7jeVXWyTcnNzJUl1SvjfXVBQoIKCgsKv8/LyXGmXH9WpI113ndetAJxRVlZI5EVFkSGIB2RFdJAHSAQch5SMDIDbXJuY0jAMDR8+XOecc45atmwZdp1x48YpEAgUPjIzM91qnu/89JP097+bSyCe2MkKibyoKDIEfkdWRA95gHjHcUjpyAC4zbUixO233641a9bo1VdfLXGdkSNHKjc3t/Cxbds2t5rnO9u2Sbffbi6BeGInKyTyoqLIEPgdWRE95AHiHcchpSMD4DZXLscYOnSo5s6dq2XLlqlBgwYlrpeSkqKUlBQ3mgQgBtnNCom8ABIZWQHALo5DgNjjaBHCMAwNHTpUc+bM0ZIlS9SkSRMnPw6AT5EVAOwgKwDYRV4AscvRIsSQIUM0a9Ysvfvuu6pZs6Z27dolSQoEAkpLS3PyowH4CFkBwA6yAoBd5AUQuxydE2LKlCnKzc1V165dVb9+/cLH7NmznfzYhFCzptStm7kE/I6scB8ZAj8iK5xBHiAekRf2kQFwm+OXY8AZJ50kffSR160AooOscB8ZAj8iK5xBHiAekRf2kQFwm2t3x0B0HT4s5eWZSwCIFBkCwEIeAImNDIDbKEL41OrVUiBgLgEgUmQIAAt5ACQ2MgBuowgBAAAAAABcQRECAAAAAAC4giIEAAAAAABwhaN3xwAAxL6sLCk317wedOZMr1sDAACAeEYRwqdatZJ275aOOcbrlgDwo+AMyc2V5s6Vevf2ulUAvECfAkhsZADcRhHCp6pUkY491utWAPArMgSAhTwAEhsZALcxJ4RPbdpknrXctMnrlgDwIzIEgIU8ABIbGQC3UYTwqdxc6b33zCUARIoMAWAhD4DERgbAbRQhAAAAAACAKyhCAAAAAAAAV1CEAAAAAAAArqAI4VMnnCA99ZS5BIBIkSEALOQBkNjIALiNW3T6VL160vDhXrcCgF+RIQAs5AGQ2MgAuI2RED7188/SG2+YSwCIFBkCwEIeAImNDIDbKEL41ObN0jXXmEsAiBQZAsBCHgCJjQyA2yhCAAAkSYGAlJXldSsAAAAQzyhCAAAkSTNnSrm5XrcCAAAA8YwiBAAAAAAAcAVFCJ9KS5PatjWXABApMgSAhTwAEhsZALdxi06fat5cys72uhUA/IoMAWAhD4DERgbAbYyEAAAAAAAArqAI4VOrVkkpKeYSACJFhgCwkAdAYiMD4DaKED5lGNLBg+YSACJFhgCwkAdAYiMD4DaKEAAAAAAAwBUUIQAAAAAAgCt8cHeMtsrJSVaNGl63I7asXx+6BCz5+dQWAQAAAMQmHxQhstWli9dtiF3XXed1CxB7qNihbM2bS+vWSU2bet0SAF4jD4DERgbAbY4WIZYtW6bx48dr5cqV2rlzp+bMmaPLL788wq2009Kly1SDoRCALfn5+b4r3EUnKxCJtDTptNO8bgUQOfIi+sgDxCOywj4yAG5ztAhx4MABtW7dWgMGDNBVV11Vzq2sUps2R1SrVlSb5nvffy899pj08MNSo0ZetwaxJC/viNdNiFh0sgKRCM4QwE/Ii+ijT4F4RFbYRwbAbY4WIXr06KEePXo4+REJa+9eaepU6bbbCAv4H1nhvuAMAfyEvIg++hSIR2SFfWQA3BZTc0IUFBSooKCg8Ou8vDwPWwMglpEXAOwgKwDYQVYA7ompafTHjRunQCBQ+MjMzPS6SQBiFHkBwA6yAoAdZAXgnpgaCTFy5EgNHz688Ou8vDwCAEBY5AUAO8JnRW9u/x2E236jNIly62/6FYB7YqoIkZKSopSUFK+b4Qv16kn3328ugUREXlQMGYJEET4ruP13ONz2G+ElRrUukfsV9AngtpgqQsC+E06Qxo3zuhUA/IoMQWLj9t+AXX689TciQ58AbnO0CJGfn6+NGzcWfr1582bl5OSoTp06atiwoZMfHff275dWrpTat5dq1vS6NUDFkBXuC84QwE+ikxfc/jsYfQqUxo+3/pboW0SCDIDbHC1CrFixQueff37h19Z1VjfccIOmT5/u5EfHvf/9Tzr/fDMw2rXzujVAxZAV7gvOkGCBgNS7t7mcOdObtgGlIS+ijz4F4lF0sqJtQswfs369eTnWyy9LzZt73Rr4md05ZBwtQnTt2lWGYTj5EQDiAFkRO6zCQ+/e3rYDKAl5AcCO6GRFYs0fw7wwqDh7FTvmhAAAAACAYhJj/hhGQiBa7M4hQxECAAAAAIpJrPljmjfnkixUjN05ZBLjxr9xqEoVcybbKlW8bgkAPyJDAFjIAyCxkQFwGyMhfKpVK+mHH7xuBQC/IkMAWMgDILGRAXAbIyEAAAAAAIArKEL41Nq1UoMG5hIAIkWGALCQB0BiIwPgNooQPvXHH9L27eYSACJFhgCwkAdAYiMD4DaKEAAAAAAAwBUUIQAAAAAAgCsoQgAAAAAAAFdwi06fOukkafFicwnA0lY5OcmqUcPrdsS+Awek5583l7m5UnZ26OvhnkN8yc/nPARM9CmAxEYGwG0UIXyqZk2pa1evWwHEmmx16eJ1G/ypfXt7zyGeUK2DiT4FkNjIALiNIoRPbd8uTZ4s3X67dMIJXrcGiBXttHTpMtVgKESZdu+WZs+W+vaVxo2TJk4Mv97DD0v5+VKNGtJjj7nbRjgrPz+foh0k0acAEh0ZALdRhPCpH3+UHn9c6tOHsACOWqU2bY6oVi2v2xH7srOl6dOl776TGjaU2rULv94HH5jL3r1LXgf+lJd3xOsmIEbQpwASGxkAt1GEAIAENnEixQUAAAC4hyIEAAAAAKBQVpY5SXUgIM2c6XVrEG+YGhsAAAAAUCg3V5o71/x3Vpa3bUH8oQjhU+np0k03mUsAiFR6ujkXhN0MCQTohADxij4FkNhKy4CZM82CBBBNXI4Rgw4cKHudjAxzApmMDOfbAyD+NGoktW5tLu2YOdOcnBJA/GnUSPrnP71uBQCvkAFwG0WIGGT37oK1akn//rd06qnOtgdA/PntN2n/fnOZluZ1awB46bffzDvlNG1KHgCJKDgDbr756FwQgFO4HMPH8vKkq66SDh70uiUA/Gb9emnJEnMJILGtXy+1bEkeAIkqOAOsuSCYjBJOoggRg/Lzy3589pm57n//K40Y4W17AQAAAMSnQMC8JJO5oRAtXI4Rg6pXL3sda7jk8OHS3/4mnXuudPXVZb+P2+0AqChyBAAAf7Az19xvv5X+uvW3nrmhEC0UIXzummukzZulG2+U2rSRmjUr/QDBGmJFiAAoL3IE8I9oHIAA8C+7c83ZYY2I4CQEKooihE8lJUlVq0rJyeZstu3bS336SF98EXqAwBlLAOEkJR19lBedESD2RXIAsmePc+0AENuSbVykX3REhHV5Bn0ARIoihE+1bSsVFBz9+s03pbPOku68M3Q9zlgCicfOmc+TT5a6djWzpLwYngnEj4wM6ZVXpG7dQp/nIAPwt/z8std5912pXz/p22/tbTP4JITECQlEjiJEnGjdWpo8WRo06OhBBbfWARJTJGc+582TevZ0ri0AvGXnAEQyCxC33CINHix17Bg6kjI311yH0ZWA/9iZa+4vfzFPWg4dKnXoUPb64fZ/TkggEtwdw6fWr5fatQu9ndZNN5kdhDVrpG++MQOCTgKAklSubE5ou2qVvfWLnvkAEPuqVy/7sXWr9I9/SM2bmwchR46Ev02f9ZxVlAAQH9avN48dJPM4wjAi3wZ30EAkKEL41G+/mQcOwZNJJSVJU6aYd87o0yd0SHbRg4dAILKQyMoiWAC/sHub30OHpMaNpV69zIOQssycGf7e4cF5QlYA/mP1KUaMkFaulKZODX3dTgGSfR/wr99+k9aulUaOlHbtkmbNinwbVh+hIkVKciRxuFKEeO6559SkSROlpqaqffv2+vTTT9342ISTlSX9+c/S+edLW7ZIt956tJJZ9OAh+Fruoo9wO364sx8EBaKNrIgOO2c+rdv8PvOMOcltr17l7zjMnHn0vVZWSEfzgaxAtJEVzmjd2txPH3hA+uOPo88X7UMEn/G09m/Jfj/Beh/gNLIiMu+9Z56cGDpU2rmzYtsqz37OaKvE4XgRYvbs2brzzjv14IMPatWqVTr33HPVo0cPbbVz2g0RsXbcuXOl5583OwsvvVTy+lanougjXAci3NkPggLRRFZ4Iz1d+vBD6YcfzEszgg88IlH0TGnwGRGyAtFEVjjriSek33+XNmwoeR1r/7aEGyFlHXyE2/etXACcRFZEbuJEacUKqUoVc36Y8lyWYQnezzkZgaIcL0JMmDBBN910kwYOHKjmzZvr6aefVmZmpqZMmeL0RyeMcIWC664zJ6m8/XZp9erItmcdTASf2Shtbong9UsLl0gDyM76hFr8ICu807y5NGeOtHSpOTFdeTodJV2qAUQbWeGse++VMjPNEZVr15a+bri5p6w+gfV68HPWo6x5ZfjbjmggK8onPd2cI2buXHPC2kiFu3yr6CjJskZgI/45eneMgwcPauXKlbr//vtDnu/WrZu++OKLYusXFBSoIOi+k3l5eU42z9eaNJFef91cBu/YwZ55RvrqK3N+iBUrpFq17G27rIJDVlboOsH/Dh6WGe69c+eGX8eaabvobNzh1g+elbuityC1Pq/odiN5r533MaN46SLNCom8qKgmTaT27c2lZN6u86WXzP+rTZtKDz0Unc8J7oQEH5wEvxZ8RjQa+2Hwfl2ebSJ2kRXOKNqnyMmRWrWS7rhDWrTInHPKrnD7mp2Z9Iv+PY6X24vz998bZEVkivYJrrjCvMT7jjukCy+U6te3v61wxwXBoySLCu7nB89dZ+dYoaS/+8HvkcruE5TWbyj6edy2ODocLULs2bNHhw8fVr169UKer1evnnbt2lVs/XHjxumRRx4p8uwknXRSjYj+ACaaypXDH6SnpUlvvGGGysCB0uzZkXUkwrF2vpLOZNjZIUsLoOBCRUmBZTes7LA+r+h2I3lv06bScceZv4d27aTsbHPCP+noc+vWSS1bSp9/LjVocPT14HWys82v27U7+pq1rcr/f08N975gjRpJY8fa/x5iRaRZIZEX0VCtmlS79tGvr7tO2rxZevhh85rQ666r+GeUVLCUQodrBz8XyX5o7VuLF5v7oVR83wh+DaY+fcgKsiK8atXMeWKeeUbq0cPsR1xzTfQ/J1xRsugJlfL+bXdbuL/7luB+TdG//5bg9wRvq6T1SusbOOGyy1Kc/QAHkBWRq1xZuuGGo8cRzz4rnXaaeVnGO++U7xiivMcFdo8Vgo8Hwp2QtdvPCN5mbm5of93a7z7/PPT4p3fv0H2xpGOA0vbp0ri1f0dTJH2LJMOoyNU+pduxY4dOOOEEffHFF+rYsWPh82PHjtXMmTP17bffhqwfrgKZmZmp3Nxc1bJ7Gj9B/PijOUSqXz+pSL6qd+/Qne3NN83/FJMnS0OGuNtOL1R0hILd5yzWzzvcOtZrwetIxavE1vuk8JXY0t4XLBCQ/v73PAUCAV/tN5FmhUReVNSPP0oXXGCe5QzOEMMwb/f78svSggXmCAmvRHJmApHLyyMr/PJ9Oy24T3HPPUf3rbw8864Z69ebE9omonBnQYuOoAzudxXtg9kZcWl3VKZXZ2TJCv983+UVnAGDBh39P/zOO+aoiJkzo3NiItZF2l+3MzqjvP0Uv464sJsXjo6EyMjIUKVKlYpVHHfv3l2sMilJKSkpSknxX7XVC9u3S3ffbR4ghPlRhrj6anM41V13SWeeKZ1xhitNdExZO2Ukl2iEW7foc8FV1OAqbNHPj2a1tyLv8+PowUizQiIvKmr7dvOe4Nu3h2ZIUpJ5Lei2bWbH44svzDkjosnOH+ayzl6UdNYD8Y2scEZwnyK4iP7001KLFtLjj0uPPeZpE6MuuC9RWiYVvTQ03AjKcCNDg7dZVlbZPcgoT78jUZEVkQnOgGCXXy795S/mccQFF0jHH+9J86Ii3IkNKfy+X1q/O1z/pLz9+9LE+/7taBGiatWqat++vRYuXKgrrrii8PmFCxfqsssuc/KjUcT48dLy5eaQyuzs0CHYfhPumq+i1clIhZvrInhinaK3Ng13SUhJ27UzCVeiIyu8c9ddUsOGof/3q1Y1R1Cdc47Us6eZHWUVOyMRbr6X0s4mWOL9DzLKRla4q2lTc2TE+PHSgAHm135VNFusf5c0vNtS2rXs4Z4ruk14g6yInkmTjl6W8e67Fb+0203h5poJfk0qXmQsC32R6HC0CCFJw4cPV1ZWljp06KCOHTvqhRde0NatWzV48GCnPxpBqlY154Ro107q37/813bFKjujH0o66xH8XFmT51jKe9YCJSMrvDFxojRmTPHnAwHpgw+ks8+WLr1UWrLEvFY8mkqavIqOO0pDVrhr5Ehpxgxp+HCz7+BX4UY0OFHo5O9+7CAroiM9XXr+eXNUxMsve3c3i9JOUpR02VRpfYqyRj7AWY4XIfr27au9e/fq0Ucf1c6dO9WyZUvNmzdPjRo1cvqjUUTjxtK//mUeUEyYYA678quSZtkvTbizHsEIIG+RFbGnYUOzEHHuueZwzLfekipVcuaz2P9gF1nhrurVpaeekvr2lT76SLrkEq9bVDFkTeIgK6LnssvM+SKsu2V4cVlGaSccS7tsCrHJ8SKEJN1222267bbb3PiohBEImMWESIf5/+lP5j3A77vPPMPZubMz7YumcJVPp26lCW+RFe6xmyFt25qjqHr3Ns+EPvOMO+0DSkNWRFdZedCnj/Tcc9KwYdKaNeboylhjd44HJBaywh47fYJJk6RPPpFuucU8wI/FEdXs7/7hShEC0XfiiSVX+MLNZRDsr381J5vr29ec9frYY51ta0WFq26WJPg1rssESlZahhTVq5f0979Lt95qXhM+bFj5PrOic7cAcEZZeZCUZB6AtG1rLu+5x722FVVSgSF4vqiS5p0BEF5wBpR0HFGnjjlxtdeXZViKzvcAf6EI4VN//CH98ot0zDFSlSqhrwXPbB1OlSrSa69JbdqYt9v58EMpOdnZ9pZHuHkbysL1XYA9wRlix+DB0nffHZ3IMmieL9usAwMAsaW0PoXl9NOl226THnnEHJZdv767bbREcgcs+gGAPcEZUNpxRCxclmGhT+FvMXjoCTvWrpXq1jWXJbHu+BDOCSeY9wNeuFD6v/9zpo0VZYULnQgg+uxkSFGPP27e8vcvf5H+/e/S17XOQGZlRTbrNAD32c2DRx+VUlOl++93p12R4o5UQPlE0ieYNMnMgVtukQzD+bYVxX4eHxgJEcdmziz9TEG3btLDD0ujR0udOpn3//Uaw7WB2JWcbE5ue+GF5rWjy5eXfMu+omcrOVsB+F/t2tLYsebBx+DBUseO0dt2NOZx4KQF4Lw6dcy7ZVx2mbnPXX99xbYXbt8PvtTCEsnIaMQ+RkIkuFGjpK5dzTObO3d63ZrQoVUUIoDYk5pq3ic8EJB69pT27fO6RQDcdNNN5u2+hw6VDh+O3natv/9FDzyko6Opwp39ZKQV4L7evc1LuocNk7Zvr9i2wu371nPBD4oP8YUiRIKrVEmaNcucdOrPf5YOHfK6RaaZMwkbIFZlZEjz5kl79phzQxQUeN0iAG6pVEl69llp5UrppZec/azgiSWLHogE36qbAxTAfc884+1lGfA3ihBQvXrmRJWffiqNGePe51rXigPwn5NOMkdE/Pvf0o030gEB4o11kB/u73SnTubzDzwg/fxz9D+7aPEhXIFh5kyKD4CX6tSRXnhB+uAD81JNIBLMCeFTrVubQ5WqV4/O9rp0MW/d+cADUufOUo8eFdte0dvmhOskWNd/lXY7UQDOiEaGdO5s7rfXXCM1aWJmSFEMkQZiX7g8KOtOW088Ic2ZY84rNWlSdNvDrPeAu8rbJ7j0UrPPP2yYdNFF5sT3gB0UIXyqUiWpVq2y14vkIP+++8zREFlZ0qpVUmZm+dsX3IGwOjDhJp4pq5MDwBl2M6QsffqYByP33WcWIpYsifzWugC8VZ48qF/fnFdq5Ehp0CCpVStn2gbAeRXpEzz9tPTxx9LNN0vvv29e4l1eTFCfOLgcw6f+9z/pkkvMZWms4YrhJnoqKjnZXL9aNalvX/OewdFQ9LrNcG3hdjuAu+xmiB0jRpgz5d9yi7RxI0OkAb8pbx4MGyY1aybdcUf0LskKBOgLAG6rSJ/AulvGvHnSjBkVa4d1EpM+RPxjJIRP7d8vLVhgLqMpPV16/XXp3HPNsxtPPlnxbdoJEsIGcFc0MyQpyZyobutW6aOPpDVrpNNPr/h2AbijvHlQtao5OV337tIbb5iXZlUU/QHAfeEyIJLR1NZlGXfeKV18MZdloGyMhEAxZ58tjR8vPfWUOfFcJOzcKotRD0D8GTDAPBNaq5bUq1fFb9kFwB8uuUS67DLp7rulAwe8bg2AaIlkNLVkXpZRrZp5eVZ5RkYxCiqxMBICYQ0bJi1bJt1wg5SdLTVtau99diaT4iwHEF+s2fPnzZN27DALmb16mXPM1KzpbdsAOG/CBKlFC2ncuPAT1AKIf9bdMi69VJo+3Tw5UZqi8z9wfJBYGAmBsJKSzPt/p6ebwysLCrxuEYBYlZt7tPNw/PHm7bo2bzaz49Ahb9sGwHlNm0r33GNewvndd163BoBX/vQn6frrpbvukn74ofR1mf8hsVGE8KnMTGnyZPt3sCjtft8lOeYY8xrPtWvNYZZlycpiGBXgF5FmSCRatZLeesucLXvIkOhNWAfAGdHIg5EjpWOPlYYPj167ALgjmn0C67KMm2/m7z9KxuUYPnXssWbn3q7y3gqzXTtz0qlbbzUnq+zbN/ytNiXu641Y0FY5OcmqUcPrdvhDx47Stm3mvpudXf7thHt/nTrSgw9KjzwitWkjnXVWxdqK6MvP5zwETJH2KcKpXt0cCXHtteYEtZdcEp22AXBeNDLAUrt2ZJdlIDFRhPCpffvM66979jQ7+06xCg6NG0sDB0pt2x4tNkRa0ACcl60uXbxugz+1b+/c+2+7rWLbhlOo1sEUrT7FNddI//iHOa/UmjXm3TMAxL5oH1f86U/mvHLW3TIaNKj4NhFfKEL41JYtZoFg5croFyGsSzZmzjxacLj2Wun9982zmZ06RffzgOhpp6VLl6kGQyHKtH69dN110ssvm2csJk4s/7buuqti74c38vPzKdpBUvT6FElJ0qRJ5uinZ56RRoyIWhMBOMiJ44qJE6WFC827ZcybZ+YDYKEIgWLC3YrntdfMuSHatJHWrXO/TYA9q9SmzRHVquV1O/yjeXOpYUNpzBh79wKXil+SFQiYl27BX/LyjnjdBMShVq3MYd2PPmoWOuvXL/kyTgDxy7os409/kqZNk2680esWIZZwQWiCKc8ElZZWrcyREFu3SjNmRL9tALwR6b3Ag+d/6d2bCWkBhHrkESk1VbrvPvNrKzPsZgyA2BEIlO+4QTJv133DDdLgweZlGVlZ5oO+AxgJkWDKO0Gl5YsvzErmrbcy0RyQ6DijCSCc2rWl//s/c3b8wYO9bg2Aipg5s2LzwD39tDmiunLlo4VIJrIHIyF8qnp16eyzzaXbJk+WTjzRnA2/Z08zmKhoAv7iZYYAiC2l5UF5z4LeeKM5Ye3QoUdv01eR0ZgAnONkn+CYY6TWraX5883R1IDESAjfOuUU6csvy/9+q1NRnjOZ1apJb74pdehgnu14+WUmmwH8pqIZAiB+lJYH5T0LWqmS9Oyz5mTWhw4d3ZYUur3g+SIAeMPpPkG9elL//tIrr0hNmzr3OfAPRkIkqOCOQLgzEtbZipI6BaecYk42M2uWuQQAALCu987Kkjp2NJfffiv9/PPRdaw+hlWMmDuXy7uAeDdxolS3rvTrr0dHRyFxUYTwqexsc/RBdnb5t1HaZHTWa6V1Cv78Z3NuiDvuqFg7ALgvGhkCID5EMw+KTkL5xBPS4cPSqFFH17H6GBQfgNjgRp/gmGPME5cffSS99JJznwN/oAiBCpkwQWrZUurTh1mvAQBAqPr1zdGTzz0nrVnjdWsAeKlnT2nAAGn4cGnbNq9bAy9RhECFpKZKb7wh7d1rTkLF8CoAAOJLRSeUbNJEOukkc+Qk/QQgsU2YINWsKQ0cSB4kMiamRGHnIvjrSDRtKk2fLl1xhTRpkjRsWFSbBwAAPFTR23snJ5v9g0suMU9cXHNN9NoGOKutcnKSVaOG1+1w1vr1ocuicnMjv1Tj4YfN5WOPFX//ffeZRckpU8y7ciB+5OfbG+NAEQJRuR7z8svNoVX33COddRaBAgAAjurWTbrsMunuu6Vevbg9MPwiW126eN0G91x3XcmvtW9fvm3Om1fy+4cMKd82EcvsVewcLUKMHTtWH3zwgXJyclS1alX98ssvTn5cQmnRQvrf/6QGDbxuyVGPP27e3qdvX7PamZ7udYvgF2SF+2IxQ4CykBXOcCsPJkwwP2vcOOmvf3X2s98BlQEAACAASURBVJDYopcV7bR06TLViPOhEAUF0u7d5t0rUlKKv37XXebdLSJx113mcuLE8r0f/pSfn2+rcOdoEeLgwYPq06ePOnbsqKlTpzr5UQknNVVq1szrVoSqUkWaPVtq21a6/nrpvffMIZhAWcgK98VihgBlISuc4UQeBF/qaV3m2bSpNGKENH68OY9U06bR/UzAEr2sWKU2bY6oVq2oNc2XGjaUxowx92W7I6it/b5dO/Pf7do51jzEkLy8I7bWc7QI8cgjj0iSpk+f7uTHJKTNm81rrR57zJzwKVZkZprh1LOn2cm47z6vWwQ/ICvcV54Mycoyr+uMdN4YIFqilxWJcZ23Xdu3m9dm33qrdMIJJa9nXdf98MNSfr5Uo4aZIeHWsc6CWqzrwbt3l1580SxGPPhgdL8POMPuNd6xhH5FZMrqE0QyL0xwX4E756EkMTUnREFBgQoKCgq/zsvL87A1se3nn6VXXjHnYYilIoQk9eghPfCA2bno2FE67zyvW4R4RF5UTHkyJDdXmjvX2XYB0RY+KxLrOm+7Pvyw7HWKXtdtXe9d2jrhvP22+YAfJEa1LpH7FdE8rgjuK5R3MlvEv5gqQowbN66wcgl/e+QR6fPPpWuvlXJyzGvMgGgiLwDYET4r2ibEdd52rV9vTkj38stS8+Ylr2dd1110GW4dxA+713j7Hf0KwD0RFyHGjBlT5g769ddfq0OHDhE3ZuTIkRo+fHjh13l5ecrMzIx4O/Be5crSq69KbdpI/fpJ8+dLlSp53Sq4ycmskMgLIF54lRVc511c8+alX7dtXddddBluHcQPu9d4O41+RezJyjKX0bjTHhJLxEWI22+/Xddee22p6zRu3LhcjUlJSVFKuClZ4Uv165uFiIsuMmfBHj3a6xbBTU5mhUReuC0ri7kg4AyyIj4wZwycRlZ4z5pw1pqgsqw5H4LXB4JFXITIyMhQRkaGE21BBOrXNw/q69f3uiWlu+AC89KM0aOlzp3NggQSA1kR2yLNEOaDgFPICu9Fo09BRsBpZIVz7GZAJBNUBq8PFOXonBBbt27Vvn37tHXrVh0+fFg5OTmSpGbNmkVwHSYzWJekd29p507zEct69DAnrrrmGnNkxLHHet2i+ObHWayjkxWIRP365u22AD8hK5xhNw84qwm/ICsiE40+AaOhEAlHixCjRo3SjBkzCr9u27atJGnx4sXq2rWrza0wg3U86d7d6xYkAv/9cY1OViASeXnSl1+ad7Dhunj4BVnhDLt5wFlN+AVZEZmK9AmCi5OMhoJdjhYhpk+fHoX787ZjBusw7M5kHUuOHJGS/XeS3nf8OIt1dLICkdi40SwKrlzJJHLwD7LCGeQB4g1ZEZmKZADFSZRHTN2iM7xVzGBdirJmskbiiZVZrAEAAACgKM5LAwAAAAAAV1CEAAAAAAAArqAI4VMpKdKJJ5pLAIgUGQLAQh4AiS3SDOBOOagoH8wJgXBOO82cRAYAyiNchgQC5i22mGQKSCz0KYDEFmkG0E9ARTESAgAgyexU5OZ63QoAAADEM4oQPrVmjXTsseYSACJFhgCwkAdAYiMD4DaKED516JC0Z4+5BIBIkSEALOXNA+sSLgD+Rp8AbqMIAQAAgIhxCRcAoDwoQgAAClkzXnN2EwAAAE7g7hgAgELWjNe9e3vbDgD+wK36AACRogjhUyefLH3xhbkEgEiRIQAsFckDbtUH+B99AriNIoRP1aghdezodSsA+JXdDMnKMq/55iwnEL/oUwCJjQyA25gTwqd++EEaPtxcAkCk7GZIbq40dy5nO4F4Rp8CSGxkANxGEcKndu+WJk40lwAQKTIEgIU8ABIbGQC3UYQAAAAAAACuoAgBAAAAAABcQRECAAAAAAC4giKET2VkSLfdZi4BIFJkCAALeQAkNjIAbuMWnT7VsKH097973QoAfkWGALCQB0BiIwPgNkZC+NSvv0rZ2eYSACJVVoYEAlLv3uYSQHyjTwEkNjIAbqMI4VPffiu1b28uASBSZWXIzJnS3LnmEkB8o08BJDYyAG6jCAEAAAAAAFxBEQIAAAAAALiCIgQAAAAAAHAFRQifSk6WatY0lwAQKTIEgIU8ABIbGQC3cYtOn2rTRsrL87oVAPyKDAFgIQ+AxEYGwG3UuwAAAAAAgCscK0Js2bJFN910k5o0aaK0tDSdeOKJGj16tA4ePOjURyaUb76RTjvNXAJ+RlZ4gwyB35AVziEPEE/IisiRAXCbY5djfPvttzpy5Iief/55NWvWTOvWrdOgQYN04MABPfnkk059bML4/XczKH7/3euWABVDVniDDIHfkBXOIQ8QT8iKyJEBcJtjRYju3bure/fuhV83bdpUGzZs0JQpUwgAAIXICgB2kBUA7CArgNjn6pwQubm5qlOnjpsfCcCHyAoAdpAVAOwgK4DY4trdMTZt2qRnn31WTz31VInrFBQUqKCgoPDrPKZpBRKOnayQyAsg0ZEVAOwgK4DYE/FIiDFjxigpKanUx4oVK0Les2PHDnXv3l19+vTRwIEDS9z2uHHjFAgECh+ZmZmRf0cJomlT6d13zSUQi5zMCom8qCgyBLGCrPAeeQA/ICucQwbAbUmGYRiRvGHPnj3as2dPqes0btxYqampksyd//zzz9dZZ52l6dOnKzm55LpHuApkZmamcnNzVatWrUiaCSSsvLw8BQIBz/cbJ7NCIi+AiiIryArADrKCrADsspsXEV+OkZGRoYyMDFvrbt++Xeeff77at2+vadOmlbnzp6SkKCUlJdImJaRdu6Rp06QBA6TjjvO6NUBxTmaFRF5UFBmCWEFWeI88gB+QFc4hA+A2xyam3LFjh7p27arMzEw9+eST+umnn7Rr1y7t2rXLqY9MKDt2SA88YC4BPyMrvEGGwG/ICueQB4gnZEXkyAC4zbGJKRcsWKCNGzdq48aNatCgQchrEV4BAiCOkRUA7CArANhBVgCxz7GREP3795dhGGEfAGAhKwDYQVYAsIOsAGKfY0UIAAAAAACAYBQhfOqYY6SrrzaXABApMgSAhTwAEhsZALc5NicEnNW0qfTGG163AoBfkSEALOQBkNjIALiNkRA+dfCg9MMP5hIAIkWGALCQB0BiIwPgNooQPrVunZSZaS4BIFJkCAALeQAkNjIAbqMIAQAAAAAAXEERAgAAAAAAuIIiBAAAAAAAcAVFCAAAAAAA4Apu0elTbdpIv/8uVanidUsA+BEZAsBCHgCJjQyA2yhC+FRyspSS4nUrAPgVGQLAQh4AiY0MgNu4HMOn/vtfqWtXcwkAkSJDAFjIAyCxkQFwG0UIn8rPl5YuNZcAECkyBICFPAASGxkAt1GEAAAAAAAArqAIAQAAAAAAXEERAgAAAAAAuIIihE81bCi9+KK5BIBIkSEALOQBkNjIALiNW3T6VEaGNHCg160A4FdkCAALeQAkNjIAbmMkhE/t2SP985/mEgAiRYYAsJAHQGIjA+A2ihA+tXWrNGiQuQSASJEhACzkAZDYyAC4jSIEAAAAAABwBUUIAAAAAADgCooQAAAAAADAFRQhfKpGDalLF3MJAJEiQwBYyAMgsZEBcBu36PSpk0+WlizxuhUA/IoMAWAhD4DERgbAbYyE8KkjR6SCAnMJAJEiQwBYyAMgsZEBcBtFCJ/KyZFSU80lAESKDAFgIQ+AxEYGwG0UIQAAAAAAgCsoQgAAAAAAAFc4WoTo3bu3GjZsqNTUVNWvX19ZWVnasWOHkx8JwIfICgB2kBUA7CArgNjmaBHi/PPP1+uvv64NGzborbfe0qZNm3T11Vc7+ZEAfIisAGAHWQHADrICiG1JhmEYbn3Y3Llzdfnll6ugoEBVqlQpc/28vDwFAgHl5uaqVq1aLrTQPw4elHbvlurWlapW9bo1iCXxsN9EmhVSfHzfbiJDEA/7DFkRHeQBShMP+wxZUToyANFid7+p7FaD9u3bp1deeUWdOnWyvfOjZFWrSg0aeN0KIPrICneQIfA7siJ6yAPEM7KibGQA3OZ4EeK+++7T5MmT9euvv+rss8/W+++/X+K6BQUFKigoKPw6NzdXkllRQajNm5M0enSqHnnkdzVp4tpgFviAtb+4OMgpKiLJCom8qCgyBGQFWWEhD1AasiL+s4IMQLTYzgsjQqNHjzYklfr4+uuvC9f/6aefjA0bNhgLFiwwOnfubPTs2dM4cuRIubfNgwcPe49t27ZFuntHlZNZYXf7PHjwKPtBVvDgwcPOg6zgwYOH3UdZeRHxnBB79uzRnj17Sl2ncePGSk1NLfb8Dz/8oMzMTH3xxRfq2LFjsdeLViB/+eUXNWrUSFu3blUgEIikmXEtLy9PmZmZ2rZtW9xfoxYJfi4mwzC0f/9+HX/88UpO9u4uvE5mhURe2ME+ER4/FxNZQVYEY78ojp+JiawgK4KxX4THz8VkNy8ivhwjIyNDGRkZ5W6UpJAdPFhKSopSUlKKPR8IBBL6l1mSWrVq8XMJg5+LYuKPpZNZIZEXkWCfCI+fC1mR6L//cNgviuNnQlYk+u8/HPaL8Pi52MsLx+aE+Oqrr/TVV1/pnHPOUe3atfXdd99p1KhROvHEE0usQAJIPGQFADvICgB2kBVA7HNsTFVaWprefvttXXjhhTrllFN04403qmXLllq6dGnYKiOAxERWALCDrABgB1kBxD7HRkK0atVKixYtqtA2UlJSNHr0aAKjCH4u4fFz8adoZIXE7z8cfibh8XPxJ7LCWfxciuNn4k9khbP4uYTHzyUyEU9MCQAAAAAAUB7eTXELAAAAAAASCkUIAAAAAADgCooQAAAAAADAFRQhAAAAAACAK2K6CPHcc8+pSZMmSk1NVfv27fXpp5963STPjBs3TmeccYZq1qypunXr6vLLL9eGDRu8blbMGTdunJKSknTnnXd63RS4iKwIRV6UjaxITGRFKLKibGRF4iIvjiIr7CEv7IvZIsTs2bN155136sEHH9SqVat07rnnqkePHtq6davXTfPE0qVLNWTIEC1fvlwLFy7UoUOH1K1bNx04cMDrpsWMr7/+Wi+88IJOP/10r5sCF5EVxZEXpSMrEhNZURxZUTqyInGRF6HIirKRFxEyYtSZZ55pDB48OOS5U0891bj//vs9alFs2b17tyHJWLp0qddNiQn79+83TjrpJGPhwoVGly5djGHDhnndJLiErCgbeXEUWZG4yIqykRVHkRWJjbwoHVkRiryIXEyOhDh48KBWrlypbt26hTzfrVs3ffHFFx61Krbk5uZKkurUqeNxS2LDkCFD1KtXL1100UVeNwUuIivsIS+OIisSE1lhD1lxFFmRuMiLspEVociLyFX2ugHh7NmzR4cPH1a9evVCnq9Xr5527drlUatih2EYGj58uM455xy1bNnS6+Z47rXXXlN2dra+/vprr5sCl5EVZSMvjiIrEhdZUTay4iiyIrGRF6UjK0KRF+UTk0UIS1JSUsjXhmEUey4R3X777VqzZo0+++wzr5viuW3btmnYsGFasGCBUlNTvW4OPEJWlIy8MJEVkMiK0pAVJrICFvIiPLLiKPKi/GKyCJGRkaFKlSoVqzbu3r27WFUy0QwdOlRz587VsmXL1KBBA6+b47mVK1dq9+7dat++feFzhw8f1rJlyzR58mQVFBSoUqVKHrYQTiIrSkdeHEVWJDayonRkxVFkBciLkpEVociL8ovJOSGqVq2q9u3ba+HChSHPL1y4UJ06dfKoVd4yDEO333673n77bS1atEhNmjTxukkx4cILL9TatWuVk5NT+OjQoYP69eunnJwcdvw4R1aER14UR1YkNrIiPLKiOLIC5EVxZEV45EX5xeRICEkaPny4srKy1KFDB3Xs2FEvvPCCtm7dqsGDB3vdNE8MGTJEs2bN0rvvvquaNWsWVmcDgYDS0tI8bp13atasWex6tOrVqys9PZ3r1BIEWVEceVEcWQGyojiyojiyAhJ5URRZER55UX4xW4To27ev9u7dq0cffVQ7d+5Uy5YtNW/ePDVq1MjrpnliypQpkqSuXbuGPD9t2jT179/f/QYBMYKsKI68AIojK4ojK4DwyItQZAWiLckwDMPrRgAAAAAAgPgXk3NCAAAAAACA+EMRAgAAAAAAuIIiBAAAAAAAcAVFCAAAAAAA4AqKEAAAAAAAwBUUIQAAAAAAgCsoQgAAAAAAAFdQhAAAAAAAAK6gCAEAAAAAAFxBEQIAAAAAALiCIgQAAAAAAHAFRQgAAAAAAOAKihAAAAAAAMAVFCEAAAAAAIArKEIAAAAAAABXUIQAAAAAAACuoAgBAAAAAABcQRECAAAAAAC4giIEAAAAAABwBUUIAAAAAADgCooQAAAAAADAFRQhAAAAAACAKyhCAAAAAAAAV1CEAAAAAAAArqAIAQAAAAAAXEERAgAAAAAAuIIiBAAAAAAAcAVFCAAAAAAA4AqKEAAAAAAAwBUUIQD8P/buPD6q6v7/+DuBkIQlIzsoYRMUkB2XIipQBaFYcClKq/mKii0KCqK2oBVQobjijlWrgnGtVapSxRUCbmUJEVAEQRBkkUY0AYSwnd8f9zfDTDJJ7iRz78ydeT0fj3lcZubOvSeTnDczn3vuuQAAAADgCooQAAAAAADAFRQhAAAAAACAKyhCAAAAAAAAV1CEAAAAAAAArqAIAQAAAAAAXEERAgAAAAAAuIIiBAAAAAAAcAVFCAAAAAAA4AqKEAAAAAAAwBUUIQAAAAAAgCsoQgAAAAAAAFdQhAAAAAAAAK6gCAEAAAAAAFxBEQIAAAAAALiCIgQAAAAAAHAFRQgAAAAAAOAKihAAAAAAAMAVFCEAAAAAAIArKEIAAAAAAABXUIQAAAAAAACuoAgBAAAAAABcQRECAAAAAAC4giIEAAAAAABwBUUIAAAAAADgCooQ1dC6dWs9+OCDgfspKSn697//XeFrRo4cqfPPP9+R9px11ll68cUXbbdn4cKFSklJ0c8//2x7H1OnTlX37t0rXOd3v/udZs6caXub1WWnTXbW8bPzO+rXr5/Gjx9vu42SdODAAbVr106ffPKJ7desWrVKLVq00N69eyPaF+IPeREeeREeeQHJ+7lRHbH4e7bz89j9mTdt2qSUlBQVFBSUu05VclWSnn76aQ0cODCi17idtXAXWUFWhBPPWUERIoq2b9+uwYMHSyr/D+qhhx7S7Nmzo77vefPmaceOHRoxYoTt15x++unavn27fD5fVNsyefJkTZ8+XcXFxVHdbnXcdNNN+vDDD22t69Tv6Mknn1SrVq3Up0+fwGPr1q3TsGHD1KhRI2VlZalPnz5asGBB4PkuXbro1FNP1QMPPBD19iC2yAsLeRFeuLzIz8/XgAEDdMwxx6hhw4b64x//qD179gSeJy8Sn9dyozri9e85+HdQkezsbG3fvl2dO3eO6v5LSko0efJk3XbbbSGPv/baa+rUqZPS09PVqVMnzZ07N+T5eMxaOIesiL14zIovv/xSF110kVq3bq2UlJSQwpWfW1lBESKKmjVrpvT09ArX8fl8OuaYY6K+74cfflhXXHGFUlPt/0pr1aqlZs2aKSUlJapt6dq1q1q3bq0XXnghqtutjrp166phw4a21nXqd/TII49o1KhRIY8NGTJEhw4d0kcffaTly5ere/fuOu+887Rjx47AOldccYUef/xxHT58OOptQuyQFxbyIrzSebFt2zadc845ateunf773/9q/vz5+vLLLzVy5MiQ15EXic1ruVFd8fj3bOd3IEk1atRQs2bNVLNmzaju/7XXXlPdunV15plnBh777LPPdMkllygnJ0dffPGFcnJydPHFF+u///1vYJ14zFo4h6yIvXjMil9++UVt27bVXXfdpWbNmoV9nVtZkTBFiCNHjujuu+9Wu3btlJ6erpYtW2r69OmSrGE6v/71r5WZmRn26JF/ONJ9992n5s2bq2HDhhozZowOHjwYWGfnzp367W9/q8zMTLVp0ybsLyZ42E2bNm0kST169FBKSor69esXsi+/kpISXX/99WrSpIkyMjJ0xhlnaOnSpYHn/cNvPvzwQ5188smqXbu2Tj/9dK1duzawTmFhoT744AMNHTq0TJsKCwt1wQUXqHbt2mrfvr3efPPNMtsOHtrz1FNPKTs7W7Vr19YFF1ygmTNnhg2o3NxctW7dWj6fTyNGjNDu3btDnh86dKheeumlMq8L57vvvtNvf/tb1a9fX3Xq1NFJJ52kt99+W5I0e/bsMvv/97//HfaL0BNPPBFo+/Dhw0N+ruoMr967d6/+7//+T3Xr1lXz5s11//33h6z/9ddfq3bt2iHDzl5//XVlZGRo1apVkqwjmOvXr9eQIUMC6xQWFmr9+vWaOHGiunbtqvbt2+uuu+7SL7/8oi+//DKw3rnnnqsff/xReXl5ttqPypEX5IXX8mLevHlKS0vTY489phNPPFGnnHKKHnvsMb322mtav359YD3ywjnkRvjcCFbR+7Bq1SqlpqaqsLBQkvTTTz8pNTVVw4cPD7x+xowZ6t27d+B+pH/Ps2bNUvv27ZWRkaGmTZvqd7/7XeC50sPVJal79+6aOnVqyGP+o5f+38Orr74a8nx1hli//fbbOuGEE5SZman+/ftr06ZNIa+58sor1bVrV5WUlEiSDh48qF69eunSSy8NrPPyyy+X+T08+OCDGjBggCZNmqQOHTpo0qRJOvvss8v8vJFkLaqOrCArpPjMilNOOUX33nuvRowYUWGBxI2sSJgixKRJk3T33Xfrtttu01dffaUXX3xRTZs21S+//KJBgwapfv36Wrp0qV599VV98MEHGjt2bMjrFyxYoA0bNmjBggWaM2eOZs+eHTJEaeTIkdq0aZM++ugj/etf/9KsWbO0c+fOctuzZMkSSdIHH3yg7du36/XXXw+73p///Ge99tprmjNnjvLz89WuXTude+652rVrV8h6t956q+6//34tW7ZMNWvW1JVXXhl47uOPP1bt2rXVsWPHMtu//fbbdfHFF2vlypX6zW9+o0svvbTMtv0++eQTjR49WuPGjVNBQYEGDBgQCM1gGzZs0L///W/NmzdP8+bNU15enu66666QdU499VQtWbIk0DkqMmbMGJWUlGjRokVatWqV7r77btWtW7fS1wVbv369/vnPf+qtt97S/PnzVVBQoDFjxkS0jfLcfPPNWrBggebOnav33ntPCxcu1PLlywPPd+jQQffdd5+uvfZafffdd9q2bZuuvvpq3XXXXerSpYskadGiRTrhhBOUlZUVeF3Dhg3VsWNHPffcc9q7d68OHTqkJ554Qk2bNlWvXr0C69WqVUvdunXT4sWLo/LzgLwgL7yXFyUlJapVq1bIkaXMzExJ1u/Uj7xwDrkRPjf8KnsfOnfurIYNGwa+JCxatEgNGzbUokWLAttYuHCh+vbtG7gfyd/zsmXLdP311+uOO+7Q2rVrNX/+fJ111lmVvq602267TRdddJG++OILXXbZZfr973+vNWvWRLyd0rZs2aILL7xQv/nNb1RQUKBRo0Zp4sSJIes8/PDD2rt3b+Dx2267TYWFhZo1a1ZgncWLF+vkk08Oed1nn31W5rzvc889V59++mnIY5FkLaqOrCArqsPJrLDLlawwCaC4uNikp6ebp556qsxzTz75pKlfv77Zs2dP4LH//Oc/JjU11ezYscMYY8zll19uWrVqZQ4dOhRYZ/jw4eaSSy4xxhizdu1aI8l8/vnngefXrFljJJkHHngg8JgkM3fuXGOMMRs3bjSSzIoVK0Lac/nll5thw4YZY4zZs2ePSUtLMy+88ELg+QMHDphjjz3W3HPPPcYYYxYsWGAkmQ8++CCk/ZLMvn37jDHGPPDAA6Zt27ZlfnZJ5q9//Wvg/p49e0xKSop55513Qrb9008/GWOMueSSS8yQIUNCtnHppZcan88XuD9lyhRTu3ZtU1xcHHjs5ptvNqeddlrI67744gsjyWzatKlMu0rr0qWLmTp1atjnnn322ZD9G2PM3LlzTfCf7pQpU0yNGjXMli1bAo+98847JjU11Wzfvj2wTrdu3SptizGhv6Pdu3ebWrVqmZdffjnw/I8//mgyMzPNuHHjQl43ZMgQc+aZZ5qzzz7bDBgwwBw5ciTw3Lhx48yvf/3rMvv6/vvvTa9evUxKSoqpUaOGOfbYY8v8zRhjzAUXXGBGjhxpq/2oGHlBXngxL1avXm1q1qxp7rnnHlNSUmJ27dplLrzwQiPJ/O1vfwtZl7yIPnKj/Nzwt8fO+3DhhReasWPHGmOMGT9+vLnxxhtNo0aNzJdffmkOHjxo6tatG8gcP7t/z6+99prJysoKyZtgrVq1CnkvjTGmW7duZsqUKSE/z+jRo0PWOe2008w111wT9meuSOnfz6RJk0zHjh1D+vpf/vKXkFw1xphPP/3UpKWlmdtuu83UrFnT5OXlBZ776aefjCSzaNGikH2V/h0bY8wLL7xgatWqFfJYJFmLqiEryIpwP3NF3MyKyn5OPzeyIiFGQqxZs0YlJSU6++yzwz7XrVs31alTJ/BYnz59dOTIkZDhQyeddJJq1KgRuN+8efNAVXHNmjWqWbNmSDWpQ4cO1T6PasOGDTp48GDIxGNpaWk69dRTy1TSunbtGtI2SYH27du3TxkZGWH3Efy6OnXqqF69euVWS9euXatTTz015LHS9yVrmFK9evVC2lN6m/4jdL/88kvYfQW7/vrrNW3aNPXp00dTpkzRypUrK31NaS1btlSLFi0C93v37l3md1wVGzZs0IEDB0KGfDVo0EAnnnhimXWfeeYZrVy5Uvn5+Zo9e3bIEPBwvyNjjK699lo1adJEixcv1pIlSzRs2DCdd9552r59e8i6mZmZtt5LVI68IC+8mBcnnXSS5syZo/vvv1+1a9dWs2bN1LZtWzVt2jTkb1EiL5xAbpSfG352GjTeQAAAIABJREFU3od+/fpp4cKFkqS8vDz1799fZ511lvLy8rR06VLt27cvpK2S/b/nAQMGqFWrVmrbtq1ycnL0wgsvVKkfBPdf//1oHN1cs2aNfvWrX4X09dL78j9200036c4779SNN94YcoR23759khT2d1H6tDNjTJnHIslaVA1ZQVZUl9NZYYcbWZEQRQj/GxVOuBD2C348LS2tzHNHjhwJbKP0+tFQ3nbDtTm4ff7n/O1r1KiRfvrpp7D7qOjnCteecG2pyjb9Q7caN24cdl/BRo0apW+//VY5OTlatWqVTj75ZD3yyCOSpNTU1DJtCD4vrjz+n6O6v7NwP395vvjiC+3du1d79+4NmVhSCv87+uijjzRv3jy9/PLL6tOnj3r27KlZs2YpMzNTc+bMCVl3165dtt5LVI68IC9K80JeSNIf/vAH7dixQ1u3btWPP/6oqVOn6n//+1/gfF8/8iL6yI3yc6OibZbeXr9+/fTll19q/fr1Wr16tc4880z17dtXeXl5WrhwoXr16hVStJTs/z3Xq1dP+fn5eumll9S8eXNNnjxZ3bp1C8z3UtV8CG5/ddjNhyNHjuiTTz5RjRo19M0334Q817BhQ6WkpJT5XTRr1qxMjuzcuVNNmzYNeSySrEXVkBVkRXU5mRV2uZEVCVGEaN++vTIzM8NeUq1Tp04qKCgIuXbsJ598otTUVJ1wwgm2tt+xY0cdOnRIy5YtCzy2du3aCq/VWqtWLUmqcJbWdu3aqVatWiHn8x48eFDLli2r8Fyq0nr06KEdO3ZU+Q/Nr0OHDoHzxvyCf+ZIrF69Wi1atFCjRo1srZ+dna3Ro0fr9ddf14033qinnnpKkvXHv3v37pDfX7jr6G7evFnbtm0L3P/ss88i+h2Xp127dkpLS9Pnn38eeOynn37SunXrQtbbtWuXRo4cqVtvvVVXXHGFLr300kAVUrJ+R19//XVIsPiri6VnD05NTS3zJW316tXq0aNHtX4WWMgL8sKLeRGsadOmqlu3rl555RVlZGRowIABIc+TF9FHblSeG3beB/+53tOmTVO3bt2UlZUV8sUi+Bxvv0j+nmvWrKlzzjlH99xzj1auXBk4b16y8iF4lGFxcbE2btxYZhvB/dd/v0OHDrb2X5FOnTqF3XZp9957r9asWaO8vDy9++67evbZZwPP1apVS506ddJXX30V8prevXvr/fffD3nsvffe0+mnnx7yWKRZi8iRFWRFdTmZFXa5kRUJUYTIyMjQX/7yF/35z3/Wc889pw0bNujzzz/X008/rUsvvVQZGRm6/PLLtXr1ai1YsEDXXXedcnJyylSIy3PiiSdq0KBBuvrqq/Xf//5Xy5cv16hRoyqsdjZp0kSZmZmaP3++fvjhBxUVFZVZp06dOrrmmmt08803a/78+frqq6909dVX65dfftFVV11l++fv0aOHGjdurE8++cT2a8K57rrr9Pbbb2vmzJn65ptv9MQTT+idd96pUlVv8eLFZSZJKs/48eP17rvvauPGjcrPz9dHH30UCLzTTjtNtWvX1i233KL169frxRdfDHtNY//v+IsvvtDixYt1/fXX6+KLLy738jN21a1bV1dddZVuvvlmffjhh1q9erVGjhxZpnAwevRoZWdn669//atmzpwpY4xuuummwPP9+/fX3r17Q6560bt3b9WvXz/Q7nXr1unmm2/Wxo0bQ2bF37Rpk7Zu3apzzjmnWj8LLOQFeeHFvJCkRx99VPn5+Vq3bp0ee+wxjR07VjNmzAgZhkteOIPcqDw37LwPKSkpOuuss/T8888HZujv2rWrDhw4oA8//DDwmF8kf8/z5s3Tww8/rIKCAn333Xd67rnndOTIkcDpUL/+9a+Vm5urxYsXa/Xq1br88svLnMokSa+++qqeeeYZrVu3TlOmTNGSJUvKTBxYFaNHj9aGDRs0YcIErV27Nmw+FRQUaPLkyXr66afVp08fPfTQQxo3bpy+/fbbwDrnnntuyBdFSRo3bpzee+893X333fr66691991364MPPtD48eND1oska1E1ZAVZUV1OZsWBAwdUUFCggoICHThwQFu3blVBQUHIVbYkl7LCsdkmXHb48GEzbdo006pVK5OWlmZatmwZmKxr5cqVpn///iYjI8M0aNDAXH311Wb37t2B1wZPzOI3btw407dv38D97du3myFDhpj09HTTsmVL89xzz5WZ0EOlJiB56qmnTHZ2tklNTQ1sq/S+9u3bZ6677jrTqFEjk56ebvr06WOWLFkSeL70ZHDGGLNixQojyWzcuDHw2MSJE82IESNCfobS7THGGJ/PZ5599tlyt/3kk0+a4447zmRmZprzzz/fTJs2zTRr1izwfLgJ2x544AHTqlWrkJ8pKyvLfPbZZ8aOsWPHmuOPP96kp6ebxo0bm5ycHFNYWBh4fu7cuaZdu3YmIyPDnHfeeebJJ58sM9Fct27dzKxZs8yxxx5rMjIyzIUXXmh27dpVYbvLU/p3tHv3bnPZZZeZ2rVrm6ZNm5p77rnH9O3bNzDR3Jw5c0ydOnXMunXrAq9ZtmyZqVWrlvnPf/4TeGzEiBFm4sSJIftaunSpGThwoGnQoIGpV6+e+dWvfmXefvvtkHX+9re/mXPPPddW22EPeUFeeDEvcnJyTIMGDUytWrVM165dzXPPPVemPeSFc8iNynOjsvfBGGMeeeQRI8nMmzcv8NiwYcNMjRo1TFFRUci6kfw9L1682PTt29fUr1/fZGZmmq5du5pXXnkl8HxRUZG5+OKLTVZWlsnOzjazZ88OO9ncY489ZgYMGGDS09NNq1atzEsvvVThz1yecJMBvvXWW6Zdu3YmPT3dnHnmmeaZZ54JvPf79u0znTp1Mn/84x9DtnPBBReY008/PTBR4Zo1a0xmZqb5+eefQ9Z79dVXzYknnmjS0tJMhw4dzGuvvRbyfKRZi6ojK8iKcD9zedzMCv++St+C/77cyoqEKUIkux07dpiGDRtGfRbTUaNGmTPOOCOi1zz66KNmwIABUW1HdU2cONH06dMnpm1YuXKladKkSbmz8Yazf/9+k52dbT7++GMHW4ZkQ15UjLwAynIqN8oTj3/P+/fvN5LM+++/H9N2DB8+vMxVcSoTj1mLxERWkBV2JMTpGLDOEX766ae1efPmam3nvvvu0xdffKH169frkUce0Zw5c3T55ZdHtI20tLTARHGxZozRhg0b9OGHH+qkk06KaVu6dOmie+65R5s2bbL9mu+++0633nprmRmAgeogL8IjL4DyRSs37Iq3v+fi4mK99NJLSk1Njcp539Vx7733qm7duhG9Jp6yFomNrCArbHG8zAFPGT58uGncuLHJyMgwnTp1Mo8//ni1tzlo0CBTp06dsLfp06dHodXl++mnn0ytWrVMnz59AhXZ8tpSp06dCq+nCyAUeUFeAG5ZtGhRhf3RaePHjzdNmjQx9957rzHGmOnTp5fblkGDBjneHgDhkRXekGJMBNcUA6pg69atITO/B2vQoIEaNGjgantKT74S7Ljjjqtwch8AziIvAISzb98+bd26tdzn27Vr52JrrKvc+C9jV1pmZqaOO+44V9sDwEJWeIOjRYjHH39cjz/+eGA46UknnaTJkydr8ODBTu0SgAeRFQDsICsA2EVeAPHL0SLEW2+9pRo1agQqTnPmzNG9996rFStWxPx8WwDxg6wAYAdZAcAu8gKIX66fjtGgQQPde++9EV1zFkDyISsA2EFWALCLvADiQ023dnT48GG9+uqr2rt3r3r37u3WbgF4DFkBwA6yAoBd5AUQXxwvQqxatUq9e/fW/v37VbduXc2dO1edOnUKu25JSYlKSkoC948cOaJdu3apYcOGSklJcbqpQEIwxmj37t069thjlZrqnavwRpIVEnkBVBdZQVYAdng1KyS+hwBus50XTl9+o6SkxHzzzTdm6dKlZuLEiaZRo0bmyy+/DLvulClTjCRuEd16GMn8/2Ws28Itnm5btmxxuntHVSRZYUyy5AX9m5vzN7IiWW/B+fLG/3/sDRf3z81rN69lhTF8D3HvFi5PZMiU5L1Vlheuzwlxzjnn6Pjjj9cTTzxR5rnSFciioiK1bNlSW7ZsUVZWlpvN9IyCglT17VtXeXl71L37kVg3B3GguLhY2dnZ+vnnn+Xz+WLdnCqrKCuk5MgL+jecRFYkTlZURXC+3HVXul5+eZ9GjMjUyy+Hv0QukleiZIXE9xCnhMsTSWRKErKbF67NCeFnjAnp4MHS09OVnp5e5vGsrCw6fzm6d5fmz5e6d68r3iIE8/rQwYqyQkqOvKB/ww1kRXIKzpeaNaWsrLTAEgjH61kh8T3EKeHyRBKZksQqywtHixC33HKLBg8erOzsbO3evVsvv/yyFi5cqPnz5zu526SSlSWde26sWwFUD1kRHv0bCEVWRE+4fPH5pJwcKTc3Nm0Coom8cA+fVxApR4sQP/zwg3JycrR9+3b5fD517dpV8+fP14ABA5zcbVLZvl164gnpT3+SmjePdWuAqiErwqN/A6HIiugJzhe/3Fxp6NDYtQmIJvLCPeHyBKiIo0WIp59+2snNQ1anv/1260MDX1LgVWRFePRvIBRZET3B+QIkIvLCPeQJIuWt6+wAAAAAAOKez2cVJnJyYt0SxBvXJ6YEAAAAACQ2//wyjJBAaYyEAAAAAAAArqAI4XH160uXXmotASQW+jcAp5AvAKKFPEGkOB3D49q0kZ5/PtatAOAE+jcAp5AvAKKFPEGkGAnhcfv3S+vXW0sAiYX+DcAp5AuAaCFPECmKEB731VdS+/bWEkBioX8DcAr5AiBayBNEiiIEAAAAAABwBUUIAAAAAADgCooQAAAAAADAFRQhAAAAIEny+aShQ6WcnFi3BACQqLhEp8f17CkZE+tWAHAC/RuAU8rLl9xcazl0qLvtAeBdfF5BpBgJAQAAAAAAXEERwuPWrpV697aWABIL/RuAU8gXANFCniBSFCE8bu9e6fPPrSWAxEL/BuAU8gVAtJAniBRFCAAAAAAA4AqKEAAAAAAAwBUUIQAAAAAAgCsoQnhc69bW5bRat451SwBEG/0bgFPIFwDRQp4gUjVj3QBUT4MG0mWXxboVAJxA/wbgFPIFQLSQJ4gUIyE87n//kx57zFoCSCz0bwBOIV8ARAt5gkhRhPC4LVuksWOtJYDEQv8G4BTyBUC0kCeIFEUIAAAAAADgCooQAAAAAADAFRQhAAAAAACAKyhCeFy9etLAgdYSQGKhfwNwCvkCIFrIE0SKS3R6XPv20rvvxroVAJxA/wbgFPIFQLSQJ4gUIyE87vBhqbjYWgJILPRvAE4hXwBEC3mCSFGE8LgvvpB8PmsJILHQvwE4hXwBEC3kCSLlaBFixowZOuWUU1SvXj01adJE559/vtauXevkLgF4EFkBwA6yAoBd5AUQvxwtQuTl5WnMmDH6/PPP9f777+vQoUMaOHCg9u7d6+RuAXgMWQHADrICgF3kBRC/HJ2Ycv78+SH3n332WTVp0kTLly/XWWed5eSuAXgIWQHADrICgF3kBRC/XL06RlFRkSSpQYMGYZ8vKSlRSUlJ4H5xcbEr7QIQXyrLCom8AEBWALCP7yFA/HBtYkpjjCZMmKAzzjhDnTt3DrvOjBkz5PP5Arfs7Gy3mudZXbpIO3daSyAR2MkKKTnygv4NlI+sqJ7K8sXnk4YOlXJy3G0X4AS+hziLzyuIlGtFiLFjx2rlypV66aWXyl1n0qRJKioqCty2bNniVvM8Ky1NatzYWgKJwE5WSMmRF/RvoHxkRfVUli+5udKbb0r//+Ax4Gl8D3EWn1cQKVeKENddd53efPNNLViwQC1atCh3vfT0dGVlZYXcULENG6wjFRs2xLolQPXZzQopOfKC/g2ER1ZUH/mCZMH3EOeRJ4iUo0UIY4zGjh2r119/XR999JHatGnj5O6SUlGR9NZbHKmAt5EV4dG/gVBkRfSQL0h05IV7yBNEytGJKceMGaMXX3xRb7zxhurVq6cdO3ZIknw+nzIzM53cNQAPISsA2EFWALCLvADil6MjIR5//HEVFRWpX79+at68eeD2yiuvOLlbAB5DVgCwg6wAYBd5AcQvR0dCGGOc3DyABEFWALCDrABgF3kBxC/Xro4BZxx3nHT//dYSQGKhfwNwCvkCIFrIE0TK0ZEQcF7TptKECbFuBQAn0L8BOIV8ARAtleWJz2ddPcPnsy7/CzASwuN++kl69VVrCSCx0L8BOIV8ARAtleVJbq705ptcPQNHUYTwuI0bpYsvtpYAEgv9G4BTyBcA0UKeIFIUIQAAAAAAgCsoQgAAAAAAAFdQhAAAAAAAAK6gCOFxmZlSjx7WEkBioX8DcAr5AiBayBNEikt0elzHjlJ+fqxbAcAJ9G8ATiFfAEQLeYJIMRICAAAAAAC4giKEx61YIaWnW0sAiYX+DcAp5AuAaCFPECmKEB5njHTggLUEkFjo3wCcQr4AiBbyBJGiCAEAAAAAAFxBEQIAAAAAALiCIgQAAAAAAHAFl+j0uI4dpdWrpbZtY90SANFG/wbgFPIFQLSQJ4gURQiPy8yUTjop1q0A4AT6NwCnkC8AooU8QaQ4HcPjvvtOGjXKWgJILPRvAE4hXwBEC3mCSFGE8Lgff5SeftpaAkgs9G8ATiFfAEQLeYJIUYQAAAAAAACuoAgBAAAAAABcQRECAAAAAAC4giKExzVtKk2caC0BJBb6NwCnkC8AooU8QaQoQnjcccdJM2ZYSwCJhf4NwCnHHSd9/710zTWSz1f+ej6flJPjXrsAeA+fVxApihAet3u3tHChtQSQWOjfAJyye7f07bfSCy9Iubnlr5ebKxUVudcuAN7D5xVEiiKEx33zjdS/v7UEkFjo3wCc8s030qefki8Aqo/PK4hUzVg3AACip4cKClJVt26s2xEda9aELoFo2rOH4xAAAPf4T++qaPQVkgNFCAAJJF99+8a6DdF32WWxbgESU4JU6wAAnpCbKw0dGutWIB5QhACQQHoqL2+R6ibIUIg1a6wCxPPPSx07xro1SDR79uxJyKIdAACIb44WIRYtWqR7771Xy5cv1/bt2zV37lydf/75Tu4y6aSlWTPRpqXFuiVA1UUvK1aoe/cjysqKehNjwt+/u3aVunSJdWuQaIqLj8S6CVXCZ4voSEuTMjL4/IDERVa4Jy1NysyUJkyQsrNj3Rp4gaMnhO7du1fdunXTo48+6uRuklqXLtYltviCAi8jK8KjfwNlkRfR0aWLNGAA+YLERVa4p0sX6ZxzrCtkMN8D7HB0JMTgwYM1ePBgJ3cBIAGQFQDsIi8A2EFWxCefz5oXwuejYJHM4mpq7JKSEhUXF4fcULFVq6QWLawlkEySIS/o30D1JUNWVMWqVdL775MvgB9ZUXWR5ElurvTmm1JRkfPtQvyKqyLEjBkz5PP5ArdsTiqq1MGD0tat1hJIJsmQF/RvoPqSISuq4uBBaf9+8gXwIyuqjjxBpOKqCDFp0iQVFRUFblu2bIl1kwDEKfICgB1kBQA7yArAPXF1ic709HSlp6fHuhkAPIC8AGAHWQHADrICcE9cjYQAAAAAAACJy9GREHv27NH69esD9zdu3KiCggI1aNBALVu2tLmVHiooSFXdus600ev27pWeeMJa5ufHujWIB3v2eK+2GJ2sSDzt20sLFlhLABbyIjrat5dOP91evvhnsw++z6z2iHdkhXsiyRNAcrgIsWzZMvXv3z9wf8KECZKkyy+/XLNnz7a5lXz17Rv9tgGJy3sVu+hkReKpV0/q1y/WrQDiC3kRHfXqSQ0bWsvKlC44BBckgHhFVrgnkjzx41Kdyc3RIkS/fv1kjKnmVnoqL2+R6jIUIqydO6VXXpEuuURq0iTWrUE82LNnj+cKd9HJisSzdav06KPS2LHSccfFujVAfCAvomPrVmnNGmtJviARkRXuqUqe+AsPFDWTU1xNTBneCnXvfkRZWbFuR3zKz5dmz5auu07q2TPWrUE8KC4+EusmIEp++EG66y5p+HC+JACIrh9+kNavt5bkC4DqIE8QKe+dPA4AAICY8Q+jzsmJdUsAAF7kgZEQAAAAiBcMowYAVAcjIQAAAAAAgCsYCeFxDRtKV11lLQEkFvo3AKc0bCi1bEm+ABXroYKCVDE/fsW2b5eaNbOW+fnhn7/rLmniRKl589DniorCvwbetGePvTEOFCE8rlUr6R//iHUrADiB/g3AKa1aSd26WUsA5cn33BXHYum886r2fK9e0W8LYsVexY4ihMft2yd9+63Utq2UmRnr1gCIJvo34BSObu7fL23bJn36qZSRUf56Dz8snX66dPLJZZ/jCGZysHtkMzH1VF7eItVN5rCwYf9+6frrrbwonSe7d0tDhlhX+rruurKvveEG6YEH3GknnLdnzx5bhTuKEB63Zo1VPVy+nEt0AomG/g04haObfn36VL7OnDnlP8cRzGSQzF/AV6h79yPKyop1O+Jbfr71WSUjo+znlfvvlw4ckKZPl449tuxrfT4+4ySS4uIjttajCAEAAJIMRzfXrJEuu0x6/nmpY8eyzxsjXX65dPiwdTWM1DAHwzmCmRzsHtkESjt40Bod8fvfhy9AIHlRhAAAAEmGo5t+HTuGPwr58svSl19KCxaEPxVD4ghmsrB7ZBMo7V//kjZvliZMKH8dn8+63K/Pd/Tyv0h8FCEAAAAQsH+/NGmS9cWgX79YtwaAFxkjzZwpnX22NQluefyFh6FD3WkX4gNFCI9LSZFq1bKWABIL/RuAU1JSjt5Ke/RRacsW6Z133G8XAO8JlycffywtWya9/bb97eTkWBPeMioi8VGE8LgePaSSkli3AoAT6N8AnNKjh3W5vB49Qh//8Udp2jTpT3+SOnSITdsAeEu4PLn/fqlTJ2nQIHvb8Pms5ZtvMioiGVCEAAAAgCTpjjukI0ekKVNi3RIAXrVunVVMeOop+6M5GfmQXJL5wr8JYc0aa1KoNWti3RIA0Ub/BuCUNWukvLzQfPnmG2nWLGs+iCZNKt+Gz2cNnwaQ3ErnyYMPSo0bS5deGtt2IX5RhPC4ffukFSusJYDEQv8G4JR9+6Ti4tB8mThRat5cGj/e3jZyc63ztwEkt+A8+fFHafZsacwYKSMj1i1DvOJ0DAAAgCS3eLH0+utWYSEzM9atAeBVjz9uXRnjmmti3RLEM0ZCAAAAJLEjR6SbbpJ69ZL+8IfIXuvzWZPIcVoGgAMHpMcek/7v/6zTMYDyMBICAAAgif3zn9KSJdKCBVJqhIen/JPJMZs9gPnzpR07pBtuiHVLEO8oQnhcmzbWh4c2bWLdEgCR2Lu38nUaNJBefpn+DSD62rSxRj40by5ddJH0299K/frFulUAvMifJ//8p3WpTi7vi8pQhPC4+vWl4cOtf+fkWBNE+Xxc5gaId3Xr2lvv6aetfg4A0VS/vnTssdLzz0tbtkjvvBPrFgHwqvr1pbQ06+oYs2bFujXwAooQcczOkdIffpD+/W/rEjhFRdY1ef1DIv3nZ1KQALxr3DjprLOkdu1i3RIAieSHH6R166SFC6U//YkjlwCq7ocfpFWrpM6dpb59Y90aeAFFiDhm90ipFH4IJZfNAuLXnj2Vr/Puu9Yw6WnTrMtdAYAddg5irF8vrV0r1a4tTZkSvX0zKhNILHby5N13rfUuuURKSXG+TfA+ihAAEAN16lS+TuvW1vKFF6TJk6W2bR1tEoAEEclBjCuukJo0id6+S4/KBOBtkeTJwIHOtQOJhSJEHLNzpPT666VnnrGGQVWEIxOAdzVoYF0+7/XXY90SAIkm0ktylsd/qU6fLzrbA+A9NaP8zTL4+4vEd5lEEuGFmOCmOnUqv11+ubXuP/5R8bb8RyY4RQPwnuuuk+bOtS6fBwCV2bOn8tvHH1vrZmREZ5+5udbnDL4cAIklkjyJtuDvL3yXSSyMhPC4446TOnWS3niDiWCAROPzWZfNGzFCevtt67rby5dLNWrEumUA4pmd072aNZOaNmXkAoCKxUOekFOJhyKExx1/vLRsmbVcu7bs8+UNj+TKGUD8O/54q+ovSQ8+KJ12mtSnj3X+tn84In0ZQFUcf7x06qnW0gk+n5VPZBOQ+KKdJ6W/v5AjiYcihMcdPGgNg5o0yZof4ssvQztu6U4b/BzDmYD4dvCg9PPP0jHHWP+55+RIr7wi7dwpjR0b+h908CRwwX2f+WAAhHPwoHTggLVMS4v+9nNzQy8ZTg4BiSvaeVJRTpT+nsPBGG9yZU6IWbNmqU2bNsrIyFCvXr20ePFiN3abFFatso6KnnqqlJlpzaBf0XmZwc/5O7G/8wKxRlaE8vfvVaus+zNmSIcPS3feGdqX/f/234ILjJxDiUREVlTfqlXWZfX8+eKk0jmUk3P0s0dODp9F4Byywh1u5on/M48/T/zzRcBbHC9CvPLKKxo/frxuvfVWrVixQmeeeaYGDx6szZs3O73rpJKWJp14ojV7/vLl9l5TuhP7+T8Q8KEAbiIrKnfccVK7dtLDD0vffFP+ev4CY3mnYtGv4WVkhXeUHlLtvy+FfoEor0ABVAdZAcQvx4sQM2fO1FVXXaVRo0apY8eOevDBB5Wdna3HH3/c6V0nnRYtpA4dpFtvrd52/B8IOHoKN5EVFfMXB3v2lJo3ty7ZWZ7gkRGlR0RVdMSgsgIkRywRD8gK7yg9MjPcaMyKChRAdZAViS04Qxjd7T2Ozglx4MABLV++XBMnTgx5fODAgfr000/LrF9SUqKSkpLA/eLiYiebl3BSUqQ77pAuvlhatEg66yx7rwv+j99/v7znSr/Ofy5W6Q8M4Z7jXFCUJ9KskJIvL/zFQcmaF2LECOmDD6RzzrH3+tIf+MP1a5/v6D78BYdwz9s5z5tzwOEEsiJxlM6F4Pv+/LHzWSI4a6TQzyOhRtOrAAAgAElEQVR28yeaeUX2xQeyIvGF61/hPruUJ9z8WeGegzMcLUIUFhbq8OHDatq0acjjTZs21Y4dO8qsP2PGDN1+++2lHn1Y7dvXVUqKgw31sIMHreWAAVaHuegiqUcP6a9/lfLyZOt9q6iTVfRc8IcE/xeXip6LJBgqkp9vLXv2tP596JBUs6Z1v/R6hw6FPuZfr/Rzwa8P97pwyttWLA0fLk2fHutWRC7SrJCSIy9K92+/iy+WHnnEumTnihXW32JlIv3P1O6kUG++WXHBIlr93g2l86SirAn3XDiR5IT/91g6m/yPVTVzwr2OrEisrKgKf77ccIPUsmXVP3BX9Ut3uAMYpS1YYF36r3Zt6dtvrdf4H/Pz/30vWGDdD/4sUHrd8gRvw876bm0rXgwblh7rJkSMrHBXcJ4Ej26KtAgoVZ4n0SoYBPfR0t8jEqn/uimSzxauXB0jpVTPNcaUeUySJk2apAkTJgTuFxcXKzs7W998c7mysrIcb6cXHT4s7d1rXcP3gguk1FRp2jRpyBDpvfekc891bt+RFi+iVVH0f6HxH5H1f9Ep/WGmf//yj86Wfi749eFeF05524o1Lxfu7WaFlBx5Edy/R44MPTL40EPSKadITz0lXXONu+2q6AhmZetGW6RfgCpa358nwUup7GPlPVfR/uzkRLgZvoMfq2rmlPc6siJxsqIqSn9+8Iu0T/lHaZUeISVVPmKyvH5TWvBpGt9/b+81iJ7i4hI9+WSsW1E1ZIU7gvOkRg3rMf/pEZXliT9D/P/fVXYAo3R2+OeRyc0NHTWK2LD72cLRIkSjRo1Uo0aNMhXHnTt3lqlMSlJ6errS071XbY2lGjWk0rk4eLB0+unW3BADB9obDREv7FZDq3r94PLWq8oXpXgqPHhdpFkhJUdeBPdv/9+b/z/lXr2swsRtt1mnZtSvH5MmxlzpL0BSxUdYS59SErx+6XPTw13KONxpLaUn/wwWSU5UVrytauYkUlaRFdETnC/Bp16GO+1KCi0mVPb/cukRkJEUHMIp3e+AypAV7gr3fUSq+P/o0jlS1f/v/NlERniLo0WIWrVqqVevXnr//fd1QVCZ/f3339ewYcOc3HXS+OYbaexY6dFHjz6WkmINhenfX5o7V7rwwti1L1KlwypcNZTztBIPWRFecP9u377s89OnS6++as0F88AD7rcvmuwcLSnvaKp/WfpLVEUqWr+8c9NLPwf3kRXRE5wv4f6uwxXjgo9Ylv7A7+QISPodIkVWuCvc55VwBYFwox2iUTggI7zH8dMxJkyYoJycHJ188snq3bu3nnzySW3evFmjR492etdJYfdu67SL3btDH+/XzzqP/LbbpGHDjg6Nijd2vngQLMmBrCgrXP8O/mKQmyvdcos0ebI0erR1mV6vsnO0pKJhltGc86I624XzyIroKO/zg5+dYpxX5ntBciIr3BMuT4IzpPRnl9LPI/k4XoS45JJL9OOPP+qOO+7Q9u3b1blzZ7399ttq1aqV07tOOqU7+LRp0mmnSS+9JF12WaxbF155R1UYTpV8yAp7Sn/4v+EG6cknpRtvlObNi127oiXcCAWGWSIYWeG+8r4s0CcRz8iK+EHBAaWlGGNMrBtRnuLiYvl8PhUVFTEhTDny861zw5cvPzqra/AkaeefL61aJX39tZSWFrt2lqeiCd1QNcnabxLx5w7Xv/2C+86//mXNSDx/vrOT0TqJLHBfIvYZO5L15y6tonwBgiVrn0nWn7sqyBP42e03qS62CTFw553Sxo3SM8/EZv/+GWvLe46jKED1XXSR1LevNSrCf5ksr2CUAwAAQHKhCOFx2dnWJDDZ2eGf79LFmjn/zjulffucbUu4gkNR0dEJrfxfNvzrFBUxPAuoSGX92y8lxZqY8uuvpb//3Z222VVZIVKyRkCQBYC77OYLAFSGPEGkKEJ4XOPG0pgx1rI8t98u7djh/JeT4IJDsOBzvP3DrTnyCVTOTv/269FDuvJKacoUadcu59tmV7hcCL7iDcUHIDYiyRcAqAh5gkhRhPC4Xbuk55+v+EtH+/bSFVdIf/tb+bNgOyk3N/RIZ+n7AMKz07+DTZ8uHTokTZ3qaLOqzT8hLRkAxE6k+QIA5SFPECmKEB63aZN1VHHTporXmzxZKi6WHnrIjVZxnjcQDXb7t1/TptJf/yrNmiV99ZWTLasacgGIH5HmCwCUhzxBpBy/RCfiQ3a2NHq0dN990rXXSg0aOLs//5FOAM4Id81tSRo3TnriCWnCBOtqGW7LyQk9/cJfcPC3lVwAAABIbhQhksgtt0j/+Id0773SjBlV347/S0bpLz9S6BcjAM7x9z3/3Ap+6enS/fdLF1wg/ec/0pAh7raLAiQAAAAqwukYSaRpU+so6cMPSz/8UPXt+L9khJuEkvkegNgbNkzq31+68UbvXbITAAAAiY0ihMfVqSP96lfW0o6bbpLS0qxJKgHEt0j7t19KivTgg9I330iPPeZM26Syl90F4B1VzRcAKI08QaQoQnjciSdKn31mLe1o0EC6+Wbrcp2bN0enDUw2Bzgj0v4drGtX6eqrrUv0FhZGv21SxaOiAMS36uQLAAQjTxApihBJ6PrrrYLBnXdWbzv++R8kTsEA4tGdd0pHjlhXx3GSz8doCAAAANhDEcLj8vOtodf5+fZfU6+eNGmS9Oyz1nDtqmL+B8BZVenfwRo3lqZMsa6WsXp11bZh55SL4EkyGREFeEN18wUA/MgTRIoiRJK65hqpWTNp6tRYtwRAdVQ2CmHsWOn446UbbpCMsb9df/FBsnfKBUVJAAAA2EERIkllZEi33Sa99JK0cmWsWwOgqnJzKy4Q1KplXbLzgw+kt96yv13/fA8UFQAAABBNFCESkH+uhsrO0b7ySqlNm8jOF8/JYbg14DXnnScNGGBdsvPAgapvh6thAAAAoLooQiQg/7DoyoZPp6VZp2O88Ya0ZIm9bRcVcWQU8JqUFOmBB6SNG6WHHy5/vZyc8gsM/uIjV8MAAABAdVCE8LhOnazJJTt1qtrr//AH67W33hrddgGoPrv9287op5NOkv70J+uKGTt3Wo+VHtlQVFR+gSE3lwIkkEiq+/kBAPzIE0SKIoTHZWRI7dpZy6qoUcP6UvLBB9LChVFtGoBqstu/7Y5+uuMOKTXVmg9GOjrvAyMbgORT3c8PAOBHniBSFCE8buNG6bLLrGVVXXCBdPLJ1miI8mbP9x8xZT4IwD3R6N/BGja0TsF66inpiy+is00A3hTtfAGQvMgTRKpmrBuA6vnpJ+mFF6QJE6xJJqsiJUWaNk0aNEh65x3pN78pu47/iCkA90Sjf5d27bXS3/8ujR8v1asXnW0C3tNDBQWpqls31u2InTVrrHwZPNjKGqA8e/ZwzBIVc+LzChIbRQhIkgYOlM480xoNMWiQNWQbQOLIybGKiT6fNHOmVWw8+eSqbcs/BwUjo+Bd+erbN9ZtiA+XXRbrFiD+JXG1DoAjKEJAkjUaYvp06ayzpNdek4YPj3WLAESTfzTT0KHWkc9Bg6RFi6T9+yPfFhNUwvt6Ki9vkeom8VCINWusAsTzz0sdO8a6NYhne/bsSeKiHaOm7FizJnSJ5GV35BRFCASceab1xWTyZGueiJr8dQAJa+ZMaxbrhx6KdUuAWFih7t2PKCsr1u2IvY4dpZ49Y90KxLPi4iOxbkIMMWoqEoysgt2RU3zN9LjmzaUpU6xlNEybZg3Rfv55aeTI6GwTQNVEu38H69jROm9z+nSpd+/obx9AfHMyX4DEwagpO/73P+n116ULL5QaN451axBLdkdOUYTwuObNrdnuo6VXLytAbr9d+sMfpFq1ordtAJGJdv8u7cQTrdM0godPMt8DkByczhcgMTBqyq5zz411CxAP7I6cogjhccXF0mefWUcyoxWQd94pde5sXcbv88+PTmYHwF1O9O9gaWnSHXdIY8dK+fnWY8z3ACQHp/MFQPIgTxAproHgcevXW/M4rF8fvW126iRdeqk1THvXLmsyO76YAO6LtH/7RzHk5Njfx5/+JNWta12yE0DycOLzA4DkRJ4gUhQhENbtt1vnd23aFOuWALArN9cqGhYV2X9NzZrWyKfFi6Vt25xrGwAAACB54nQMLo1TkYouiVNUdHSIdVUMGya98YZ1GT/ef++we2kcJIecnMpPqWrcWDrvPOnDD61LdmZkuNc+AAAAJBdHixDTp0/Xf/7zHxUUFKhWrVr6+eefq7AVLo1jR3mXxOnVq/rb5v33Gu9VjKKTFQinqMgaHVGZ+++XOnSwlrfe6ny7gKogKwDYQVYA8c3RIsSBAwc0fPhw9e7dW08//XQVt8KlcSqyYYM0YYI0c6Z0/PGhz91wg/TAA9XbfjS2AXfZvTROPIlOViSe9HSrX6enO7+vE06wLtk5Y4Z0xRXSscc6v08gUmRF9LiZL4DbyAp3kSeIlKNFiNtvv12SNHv27GpshUvjVKRnT2n48PDP+XzW89URjW3AXXYvjRNPopMVieekk9yd5OmEE6wZrm+5ReJXgXhEVkSP2/kCuImscBd5gkjF1ZwQJSUlKikpCdwvLi6OYWsAxDPyonw+nzUXRHlXtSlvnoi0NOsSvddcI40ZI51yivNtBZxGVgCwg6wA3BNXM9jNmDFDPp8vcMvOzo51k+LeypXWpHIrV8a6JYC7kiEvqtq/c3MrvkKGf56IcEWKUaOkLl2sS3YaE9l+gXiUDFlRFXx+AEKRFVVHniBSERchpk6dqpSUlApvy5Ytq1JjJk2apKKiosBty5YtVdpOMjl0SCostJbVlZMjDR1qLYHqcjIrpOTIi2j2bz+fr+IrZdSsKT34oPTpp9Irr0Rvv0B5yIrYcCJfACeRFfGLPEGkIj4dY+zYsRoxYkSF67Ru3bpKjUlPT1c6M5rEjP/oqL8QUdll/YCKOJkVEnlRVeFGP/h8Vr/39/df/9q6RO+f/2w9Xru2u21EciErANhBVgCJI+IiRKNGjdSoUSMn2oIoq+y88IpeJ9m7rB9QHrLCO8JlxH33SZ06WcvJk91vE5IHWQHADrICSByOTky5efNm7dq1S5s3b9bhw4dVUFAgSWrXrh2X3HRBbq51FLMqrwPcRFbEn3btrHkh7r5buvJKqUWLWLcIICsA2ENWAPHN0SLE5MmTNWfOnMD9Hj16SJIWLFigfv36ObnrpHHCCda52yecUPVtcOoFYo2sCC8a/bs6br1VmjNHmjSJ4iTiA1kRPbHOF8BJZIW7yBNEKsWY+J3/vLi4WD6fT0VFRcrKyop1czxp6NDKT6uwsw68I1n7jfVz91Ne3iKOcki64QbpgQeOLqvq9delt96S/v53iVNlE8uePXvUt2+9JM2K5MtIoKqStc8k688NVIfdfuPoSAg47/vvpZkzpQkTwg+XDp5wrvSRTEZAIPHkq2/fWLchfvTqFbqsjtNPr/42EG8o1iWzyj4/AIBd5AkiRRHC43butI5yXnZZ+E7vLzyEmxvCfzUMIHH0TKiREGvWWH37+eeljh0je220RkIgcVkjIWLdCsRKZZ8fAMAu8gSRoggBIIGsUPfuR5RooyY7dpR69ozsNT6f9Rr/EiituPhIrJsAAACSUGqsGwAAAAAAAJIDRYgk4Z8bIicn1i0B4Ibg+WAAAACAeMHpGB7XqJF07bXWsiKl54bIyeHLCRDv7PbvcLikJoCKVCdfACAYeYJIUYTwuJYtpccei/x1TEoJxL+q9m8AqAz5AiBayBNEitMxPO6XX6T8fGsJILHQvwE4hXwBEC3kCSJFEcLjvv5a6tXLWgJILPRvAE4hXwBEC3mCSFGEAAAAAAAArqAIAQAAAAAAXEERAgAAAAAAuIIihMelpkr16llLAImF/g3AKeQLgGghTxApLtHpcd27S8XFsW4FACfQvwE4hXwBEC3kCSJFvSrJ+HzS0KHWEgAAAAAAN1GE8LivvpJOOsla2pGbK735prUEEN8i7d8AYBf5AiBayBNEiiKEx+3fb3X4/ftj3RIA0Ub/BuAU8gVAtJAniBRFCAAAAAAA4AqKEAAAAAAAwBUUIQAAAAAAgCsoQnhc27bSG29YSwCJhf4NwCnkC4BoIU8QqZqxbgCq55hjrEtuAkg89G8ATiFfAEQLeYJIMRLC43bskGbMsJYAEgv9G4BTyBcA0UKeIFIUITxu2zbpllusJYDEQv8G4BTyBUC0kCeIFEUIAAAAAADgCooQAAAAAADAFRQhAAAAAACAKyhCeNwxx0i/+521BJBY6N8AnEK+AIgW8gSR4hKdHte2rfTqq7FuBQAn0L8BOIV8ARAt5Aki5dhIiE2bNumqq65SmzZtlJmZqeOPP15TpkzRgQMHnNplUjpwQPr+e2sJeBFZUT76N3AUWRFd5AsSFVnhPvIEkXJsJMTXX3+tI0eO6IknnlC7du20evVqXX311dq7d6/uu+8+p3abdFavlnr1kpYvl3r2jHVrgMiRFeWjfwNHkRXRRb4gUZEV7iNPECnHihCDBg3SoEGDAvfbtm2rtWvX6vHHHycAAASQFQDsICsA2EFWAPHP1TkhioqK1KBBg3KfLykpUUlJSeB+cXGxG80CEGcqywqJvABAVgCwh6wA4otrV8fYsGGDHnnkEY0ePbrcdWbMmCGfzxe4ZWdnu9U8AHHCTlZI5AWQ7MgKAHaQFUD8ibgIMXXqVKWkpFR4W7ZsWchrtm3bpkGDBmn48OEaNWpUudueNGmSioqKArctW7ZE/hMBiAtOZoVEXgCJgqwAYAdZASSOFGOMieQFhYWFKiwsrHCd1q1bKyMjQ5LV+fv376/TTjtNs2fPVmqq/bpHcXGxfD6fioqKlJWVFUkzk8aRI9LBg1JamhTBW4sEFi/9xs2skOLn544m+jecFC99hqyIDfIFdsVLnyEr4hd5Aj+7/SbiOSEaNWqkRo0a2Vp369at6t+/v3r16qVnn3024s6PyqWmSunpsW4FUBZZUX30byQDsiI2yBd4DVkRv8gTRMqxHrlt2zb169dP2dnZuu+++/S///1PO3bs0I4dO5zaZVJat07q189aAl5EVpSP/g0cRVZEF/mCREVWuI88QaQcuzrGe++9p/Xr12v9+vVq0aJFyHMRngGCCuzZI+XlWUvAi8iK8tG/gaPIiugiX5CoyAr3kSeIlGMjIUaOHCljTNgbAPiRFQDsICsA2EFWAPGPE6QAAAAAAIArKEIAAAAAAABXUITwuJYtpaeespYAEgv9G4BTyBcA0UKeIFKOTUwJdzRqJI0aFetWAHAC/RuAU8gXANFCniBSjITwuMJC6R//sJYAEgv9G4BTyBcA0UKeIFIUITxu82bp6qutJYDEQv8G4BTyBUC0kCeIFEUIAAAAAADgCooQAAAAAADAFRQhAAAAAACAKyhCeFzdulLfvtYSQGKhfwNwCvkCIFrIE0SKS3R63AknSAsXxroVAJxA/wbgFPIFQLSQJ4gUIyE87sgRqaTEWgJILPRvAE4hXwBEC3mCSFGE8LiCAikjw1oCSCz0bwBOIV8ARAt5gkhRhAAAAAAAAK6gCAEAAAAAAFxBEQIAAAAAALiCIgQAAAAAAHAFl+j0uM6dpS1bpCZNYt0SANFG/wbgFPIFQLSQJ4gURQiPq1VLatEi1q0A4AT6NwCnkC8AooU8QaQ4HcPjvv1WGj7cWgJILPRvAE4hXwBEC3mCSFGE8Liff5b+9S9rCSCx0L8BOIV8ARAt5AkiRRECAAAAAAC4giIEAAAAAABwBUUIAAAAAADgCooQHnfssdLf/mYtASQW+jcAp5AvAKKFPEGkuESnxzVrJk2aFOtWAHAC/RuAU8gXANFCniBSjITwuJ9/lt58k9logURE/wbgFPIFQLSQJ4gURQiP+/ZbadgwrssLJCL6NwCnkC8AooU8QaQoQgAAAAAAAFc4WoQYOnSoWrZsqYyMDDVv3lw5OTnatm2bk7sE4EFkBQA7yAoAdpAVQHxztAjRv39//fOf/9TatWv12muvacOGDfrd737n5C4BeBBZAcAOsgKAHWQFEN8cvTrGDTfcEPh3q1atNHHiRJ1//vk6ePCg0tLSnNx10sjIkDp1spaAV5EV4dG/gVBkRfSQL0hkZIW7yBNEyrVLdO7atUsvvPCCTj/99HI7f0lJiUpKSgL3i4qKJEnFxcWutNGLWrSQPvvM+jdvE6Sj/cUYE+OWVI2drJCSIy/o33ASWZHcnYp8gV1kBR2kMuQJ/GznhXHYn//8Z1O7dm0jyfzqV78yhYWF5a47ZcoUI4kbN25RuG3ZssXp7h1VkWSFMeQFN27RupEV3Lhxs3MjK7hx42b3VllepBgTWVlz6tSpuv322ytcZ+nSpTr55JMlSYWFhdq1a5e+++473X777fL5fJo3b55SUlLKvK50BfLnn39Wq1attHnzZvl8vkiamdCKi4uVnZ2tLVu2KCsrK9bNiRu8LxZjjHbv3q1jjz1WqamxuwCOk1khkRd20CfC432xkBVkRTD6RVm8JxaygqwIRr8Ij/fFYjcvIi5CFBYWqrCwsMJ1WrdurYwwJwV9//33ys7O1qeffqrevXtXuq/i4mL5fD4VFRUl9S+zNN6X8Hhf4oubWSHx+w+H9yQ83pf4QlbEB96XsnhP4gtZER94X8LjfYlMxHNCNGrUSI0aNarSzvz1juAqI4DERFYAsIOsAGAHWQEkDscmplyyZImWLFmiM844Q/Xr19e3336ryZMn6/jjj7ddgQSQ+MgKAHaQFQDsICuA+OfYiV2ZmZl6/fXXdfbZZ+vEE0/UlVdeqc6dOysvL0/p6em2tpGenq4pU6bYXj9Z8L6Ex/viTdHIConffzi8J+HxvngTWeEs3peyeE+8iaxwFu9LeLwvkYl4TggAAAAAAICqiN0UtwAAAAAAIKlQhAAAAAAAAK6gCAEAAAAAAFxBEQIAAAAAALgirosQs2bNUps2bZSRkaFevXpp8eLFsW5SzMyYMUOnnHKK6tWrpyZNmuj888/X2rVrY92suDNjxgylpKRo/PjxsW4KXERWhCIvKkdWJCeyIhRZUTmyInmRF0eRFfaQF/bFbRHilVde0fjx43XrrbdqxYoVOvPMMzV48GBt3rw51k2Liby8PI0ZM0aff/653n//fR06dEgDBw7U3r17Y920uLF06VI9+eST6tq1a6ybAheRFWWRFxU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",
      "text/plain": [
       "<Figure size 1300x2000 with 60 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the dataset\n",
    "hddm.plotting.plot_from_data(\n",
    "    df=data,\n",
    "    generative_model=\"ddm_hddm_base\",\n",
    "    columns=4,\n",
    "    groupby=[\"condition\", \"subj_idx\"],\n",
    "    figsize=(13, 20),\n",
    "    value_range=np.arange(0, 6, 0.1),\n",
    "    plot_func=hddm.plotting._plot_func_model,\n",
    "    **{\n",
    "        \"alpha\": 1.0,\n",
    "        \"ylim\": 3,\n",
    "        \"hist_bottom\": 0,  # bottom of upper histogram\n",
    "        \"add_data_rts\": True,\n",
    "        \"add_data_model\": True,\n",
    "    }\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a245a0-6f82-48ca-a02a-6ab1cc67f915",
   "metadata": {},
   "source": [
    "##### NOTE\n",
    "\n",
    "Via the `**kwargs` you have many styling options with this plot. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e9aa19c9-f12c-442d-b7e3-286a89636609",
   "metadata": {},
   "source": [
    "### A First Model Fit\n",
    "\n",
    "Let's generate some simple data again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ca65855",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get some default parameters\n",
    "\n",
    "# The 'model_config' dictionary contains essential information about all models\n",
    "# currently included in HDDM\n",
    "params = hddm.model_config.model_config[\"ddm_hddm_base\"][\"params_default\"]\n",
    "params[3] = 0.5\n",
    "sim_out = hddm.simulators.simulator(theta=params, model=model, n_samples=n_samples)\n",
    "\n",
    "theta = np.zeros((n_samples, 5))\n",
    "theta[:, 0] = sim_out[2][\"v\"][0]\n",
    "theta[:, 1] = sim_out[2][\"a\"][0]\n",
    "theta[:, 2] = sim_out[2][\"z\"][0]\n",
    "theta[:, 3] = sim_out[2][\"t\"][0]\n",
    "theta[:, 4] = 0\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.concatenate([(sim_out[0]), (sim_out[1]), theta], axis=1),\n",
    "    columns=[\"rt\", \"response\", \"v\", \"a\", \"z\", \"t\", \"subj_idx\"],\n",
    ")\n",
    "data[\"subj_idx\"] = data[\"subj_idx\"].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "58942e9c-2728-49eb-9c69-b014bf64e1a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n"
     ]
    }
   ],
   "source": [
    "# Define the HDDM model\n",
    "includes = hddm.model_config.model_config[model][\"hddm_include\"]\n",
    "n_models = 2\n",
    "hddm_models = []\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models.append(\n",
    "        hddm.HDDM(\n",
    "            data,\n",
    "            informative=True,\n",
    "            is_group_model=False,\n",
    "            include=[\"v\", \"a\", \"z\", \"t\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bddad01e-9a70-4600-9629-aca69988626e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 21.2 sec1\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 20.0 sec"
     ]
    }
   ],
   "source": [
    "# Sample\n",
    "nmcmc = 2000\n",
    "\n",
    "for i in range(n_models):\n",
    "    print(i)\n",
    "    hddm_models[i].sample(nmcmc, burn=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94dd034e-ee34-40a2-991c-1d8cb81656bc",
   "metadata": {},
   "source": [
    "##### NOTE\n",
    "\n",
    "You can parallelize the model fits using the `multiprocessing` library for example.\n",
    "See the template below:\n",
    "\n",
    "```\n",
    "# imports ...\n",
    "from functools import partial\n",
    "from multiprocessing import Pool\n",
    "import psutil\n",
    "# etc. etc.\n",
    "\n",
    "def run_model(chain_id, data, x, y, z):\n",
    "    import hddm\n",
    "\n",
    "    # Process x,y,z ...\n",
    "  \n",
    "    # Define Model\n",
    "    model = hddm.HDDM(data,\n",
    "              informative = x,\n",
    "              is_group_model = True,\n",
    "              include = y,\n",
    "              p_outlier = z)\n",
    "    \n",
    "    # Sample\n",
    "    model.sample(1000, burn = 500, dbname = 'db_name_' + str(chain_id) + '.db', db = 'pickle')\n",
    "    \n",
    "    # Save\n",
    "    model.save('my_model_' + str(chain_id) + '.pickle')\n",
    "    \n",
    "    return 0\n",
    "    \n",
    "if __name__ == \"__main__\":\n",
    "    n_cpus = psutil.cpu_count(logical = False)\n",
    "    n_chains = ...\n",
    "    data = ...\n",
    "    x = ...\n",
    "    y = ...\n",
    "    z = ...\n",
    "    \n",
    "    run_model_prepped = partial(run_model, data = data, x = x, y = y, z = z)\n",
    "    \n",
    "    with Pool(processes = n_cpus) as pool:\n",
    "        pool_out = pool.map(run_model_prepped, [1, 2, ... , n_chains])\n",
    "    \n",
    "    print(\"Finished\")\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a2b4c0-b824-46c9-94f8-01edd7875906",
   "metadata": {},
   "source": [
    "### Reporting HDDM results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62c31e33-2f4e-4e3e-90ca-8207e9b7db77",
   "metadata": {},
   "source": [
    "#### Posterior Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "70c37758-3768-4ce7-8f01-7576fefc99f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2.09666</td>\n",
       "      <td>0.030653</td>\n",
       "      <td>2.037259</td>\n",
       "      <td>2.075024</td>\n",
       "      <td>2.095271</td>\n",
       "      <td>2.11635</td>\n",
       "      <td>2.160866</td>\n",
       "      <td>0.001213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>-0.052051</td>\n",
       "      <td>0.034457</td>\n",
       "      <td>-0.11563</td>\n",
       "      <td>-0.075865</td>\n",
       "      <td>-0.052081</td>\n",
       "      <td>-0.029168</td>\n",
       "      <td>0.014518</td>\n",
       "      <td>0.001297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.484771</td>\n",
       "      <td>0.008865</td>\n",
       "      <td>0.466659</td>\n",
       "      <td>0.478512</td>\n",
       "      <td>0.485222</td>\n",
       "      <td>0.491326</td>\n",
       "      <td>0.500522</td>\n",
       "      <td>0.000372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.493734</td>\n",
       "      <td>0.010371</td>\n",
       "      <td>0.474054</td>\n",
       "      <td>0.486568</td>\n",
       "      <td>0.49386</td>\n",
       "      <td>0.500864</td>\n",
       "      <td>0.514309</td>\n",
       "      <td>0.000448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean       std      2.5q       25q       50q       75q     97.5q  \\\n",
       "a   2.09666  0.030653  2.037259  2.075024  2.095271   2.11635  2.160866   \n",
       "v -0.052051  0.034457  -0.11563 -0.075865 -0.052081 -0.029168  0.014518   \n",
       "t  0.484771  0.008865  0.466659  0.478512  0.485222  0.491326  0.500522   \n",
       "z  0.493734  0.010371  0.474054  0.486568   0.49386  0.500864  0.514309   \n",
       "\n",
       "     mc err  \n",
       "a  0.001213  \n",
       "v  0.001297  \n",
       "t  0.000372  \n",
       "z  0.000448  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Posterior Means\n",
    "hddm_models[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e074f404-caf5-4241-9553-88d8bd7caf54",
   "metadata": {},
   "source": [
    "#### Posterior Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "720f3786-9755-4f89-aa53-0b4f04c27653",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>v</th>\n",
       "      <th>t</th>\n",
       "      <th>z_trans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.139098</td>\n",
       "      <td>-0.042891</td>\n",
       "      <td>0.478402</td>\n",
       "      <td>-0.070482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.138636</td>\n",
       "      <td>-0.062324</td>\n",
       "      <td>0.480147</td>\n",
       "      <td>0.006586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.108636</td>\n",
       "      <td>-0.048976</td>\n",
       "      <td>0.472777</td>\n",
       "      <td>-0.026763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.088197</td>\n",
       "      <td>-0.078301</td>\n",
       "      <td>0.490929</td>\n",
       "      <td>-0.041651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.096462</td>\n",
       "      <td>-0.076076</td>\n",
       "      <td>0.474411</td>\n",
       "      <td>-0.012035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.082154</td>\n",
       "      <td>-0.008345</td>\n",
       "      <td>0.484429</td>\n",
       "      <td>0.026875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.079319</td>\n",
       "      <td>-0.105173</td>\n",
       "      <td>0.485330</td>\n",
       "      <td>0.036852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.089114</td>\n",
       "      <td>-0.053570</td>\n",
       "      <td>0.489572</td>\n",
       "      <td>0.021592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.081694</td>\n",
       "      <td>-0.053034</td>\n",
       "      <td>0.483575</td>\n",
       "      <td>-0.013950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.073855</td>\n",
       "      <td>-0.055922</td>\n",
       "      <td>0.502042</td>\n",
       "      <td>-0.003141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            a         v         t   z_trans\n",
       "0    2.139098 -0.042891  0.478402 -0.070482\n",
       "1    2.138636 -0.062324  0.480147  0.006586\n",
       "2    2.108636 -0.048976  0.472777 -0.026763\n",
       "3    2.088197 -0.078301  0.490929 -0.041651\n",
       "4    2.096462 -0.076076  0.474411 -0.012035\n",
       "..        ...       ...       ...       ...\n",
       "995  2.082154 -0.008345  0.484429  0.026875\n",
       "996  2.079319 -0.105173  0.485330  0.036852\n",
       "997  2.089114 -0.053570  0.489572  0.021592\n",
       "998  2.081694 -0.053034  0.483575 -0.013950\n",
       "999  2.073855 -0.055922  0.502042 -0.003141\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A look at the traces\n",
    "hddm_models[0].get_traces()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "464606b1-314d-40cb-8888-8ec0594298cc",
   "metadata": {},
   "source": [
    "#### Convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b59bc19-ced8-483e-a69b-59c0e0e64c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 0.99992387495781,\n",
       " 'v': 0.9996920006228611,\n",
       " 't': 0.9999328375710046,\n",
       " 'z_trans': 0.9998435090611567}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from kabuki.analyze import gelman_rubin\n",
    "\n",
    "gelman_rubin(hddm_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "84b6b25d-fe01-4c00-8716-83572b6cae0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting a\n",
      "Plotting v\n",
      "Plotting t\n",
      "Plotting z\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the traces\n",
    "hddm_models[0].plot_posteriors()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00285849-5d67-41b2-b404-f96be11796cd",
   "metadata": {},
   "source": [
    "#### Other Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cfc78546-6f18-431f-9556-435d69909ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Posterior Predictive Plots\n",
    "hddm.plotting.plot_posterior_pair(\n",
    "    hddm_models[0], parameter_recovery_mode=False, samples=500, figsize=(6, 6)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "49808557-9c77-41da-952d-d4eaee4ff56b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tracename: \n",
      "v\n",
      "tracename: \n",
      "a\n",
      "tracename: \n",
      "z\n",
      "tracename: \n",
      "t\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Caterpillar plots\n",
    "hddm.plotting.plot_caterpillar(\n",
    "    hddm_model=hddm_models[0],\n",
    "    ground_truth_parameter_dict=None,\n",
    "    figsize=(5, 5),\n",
    "    columns=3,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d926f99-7f5b-44a0-843e-d85e24eada26",
   "metadata": {},
   "source": [
    "#### Posterior Predictives"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a2029d-7f24-4b81-8779-01c6cc22ad5e",
   "metadata": {},
   "source": [
    "##### Access via plotting: `plot_posterior_predictive()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "724b085f-61b8-498f-86b8-a72e13f14a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Posterior Predictive\n",
    "# FIX: IMPROVE LABELING\n",
    "hddm.plotting.plot_posterior_predictive(\n",
    "    model=hddm_models[0],\n",
    "    columns=2,  # groupby = ['subj_idx'],\n",
    "    figsize=(8, 6),\n",
    "    value_range=np.arange(-15, 15, 0.1),\n",
    "    parameter_recovery_mode=True,\n",
    "    **{\n",
    "        \"alpha\": 0.01,\n",
    "        \"ylim\": 3,\n",
    "        \"bin_size\": 0.4,\n",
    "        \"add_posterior_mean_rts\": True,\n",
    "        \"add_posterior_uncertainty_rts\": True,\n",
    "        \"samples\": 200,\n",
    "        \"legend_fontsize\": 7,\n",
    "        \"subplots_adjust\": {\"top\": 0.9, \"hspace\": 0.3, \"wspace\": 0.3},\n",
    "    }\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33079327-a347-4938-815c-b1ba1dfa47df",
   "metadata": {},
   "source": [
    "##### Lower level access: `post_pred_gen()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2877b9a0-cb45-44f2-a4a7-ecd174ab0dcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 1 of 1 complete in 0.0 sec"
     ]
    }
   ],
   "source": [
    "# Direct access to posterior predictives\n",
    "posterior_predictive_sample = hddm.utils.post_pred_gen(\n",
    "    model=hddm_models[0], samples=200, groupby=[\"subj_idx\"], append_data=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c1bbe0dc-15b3-4df8-93cf-1497d6ac91ca",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>rt_sampled</th>\n",
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       "      <th>node</th>\n",
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       "      <th rowspan=\"11\" valign=\"top\">wfpt</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
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       "      <td>-1.004997</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.213714</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.997997</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.816695</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.068017</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.463680</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.812999</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.407723</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.238994</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">199</th>\n",
       "      <th>995</th>\n",
       "      <td>-1.102949</td>\n",
       "      <td>0</td>\n",
       "      <td>995</td>\n",
       "      <td>1.162995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>-1.006951</td>\n",
       "      <td>0</td>\n",
       "      <td>996</td>\n",
       "      <td>-0.992997</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>-2.079972</td>\n",
       "      <td>0</td>\n",
       "      <td>997</td>\n",
       "      <td>0.748000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.727954</td>\n",
       "      <td>1</td>\n",
       "      <td>998</td>\n",
       "      <td>-1.385992</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>-1.325946</td>\n",
       "      <td>0</td>\n",
       "      <td>999</td>\n",
       "      <td>0.880999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 rt_sampled  response_sampled  index        rt  response    v  \\\n",
       "node sample                                                                     \n",
       "wfpt 0      0     -0.751689                 0      0 -1.004997      -1.0  0.0   \n",
       "            1     -2.213714                 0      1 -0.997997      -1.0  0.0   \n",
       "            2     -1.816695                 0      2 -2.068017      -1.0  0.0   \n",
       "            3     -1.463680                 0      3 -0.812999      -1.0  0.0   \n",
       "            4     -2.407723                 0      4 -1.238994      -1.0  0.0   \n",
       "...                     ...               ...    ...       ...       ...  ...   \n",
       "     199    995   -1.102949                 0    995  1.162995       1.0  0.0   \n",
       "            996   -1.006951                 0    996 -0.992997      -1.0  0.0   \n",
       "            997   -2.079972                 0    997  0.748000       1.0  0.0   \n",
       "            998    0.727954                 1    998 -1.385992      -1.0  0.0   \n",
       "            999   -1.325946                 0    999  0.880999       1.0  0.0   \n",
       "\n",
       "                   a    z    t subj_idx  \n",
       "node sample                              \n",
       "wfpt 0      0    2.0  0.5  0.5      0.0  \n",
       "            1    2.0  0.5  0.5      0.0  \n",
       "            2    2.0  0.5  0.5      0.0  \n",
       "            3    2.0  0.5  0.5      0.0  \n",
       "            4    2.0  0.5  0.5      0.0  \n",
       "...              ...  ...  ...      ...  \n",
       "     199    995  2.0  0.5  0.5      0.0  \n",
       "            996  2.0  0.5  0.5      0.0  \n",
       "            997  2.0  0.5  0.5      0.0  \n",
       "            998  2.0  0.5  0.5      0.0  \n",
       "            999  2.0  0.5  0.5      0.0  \n",
       "\n",
       "[200000 rows x 10 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posterior_predictive_sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96239b07-32c5-4a1b-9b17-4f5d541764b9",
   "metadata": {},
   "source": [
    "From here we can compute any quantity we want, comparing any aspect of our original dataset with the synthetic data we can generate via the posterior predictives."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d418d15-54aa-42bd-822f-e680317264e2",
   "metadata": {},
   "source": [
    "##### Lowest level access: `simulator()`\n",
    "\n",
    "Using the simulator function gives you the greates amount of flexibility. Just take posterior samples from the traces and use them as parameter (`theta`) inputs to the simulator function directly. There is essentially nothing you shouldn't be able to do."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547e6a67-9ee7-4a10-b7fc-891cb6b0ebf2",
   "metadata": {},
   "source": [
    "### More Complex Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299fb9fa-d9ac-42c7-9e6c-97fb4de1d0bf",
   "metadata": {},
   "source": [
    "#### Condition / Regression, simplest case\n",
    "\n",
    "We show how to replicate an analysis using the basic `HDDM()` class, with the `HDDMRegressor()` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "370c79e5-f133-4f0d-8e27-4670c6522615",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>my_condition</th>\n",
       "      <th>v</th>\n",
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       "      <th>0</th>\n",
       "      <td>7.049264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>-0.356632</td>\n",
       "      <td>4.13145</td>\n",
       "      <td>0.580191</td>\n",
       "      <td>1.702469</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.987486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>-0.356632</td>\n",
       "      <td>4.13145</td>\n",
       "      <td>0.580191</td>\n",
       "      <td>1.702469</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.385347</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>-0.356632</td>\n",
       "      <td>4.13145</td>\n",
       "      <td>0.580191</td>\n",
       "      <td>1.702469</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.340460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>-0.356632</td>\n",
       "      <td>4.13145</td>\n",
       "      <td>0.580191</td>\n",
       "      <td>1.702469</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.496305</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "      <td>-0.356632</td>\n",
       "      <td>4.13145</td>\n",
       "      <td>0.580191</td>\n",
       "      <td>1.702469</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>2.494462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>-2.165628</td>\n",
       "      <td>4.13145</td>\n",
       "      <td>0.580191</td>\n",
       "      <td>1.702469</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>2.534462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>-2.165628</td>\n",
       "      <td>4.13145</td>\n",
       "      <td>0.580191</td>\n",
       "      <td>1.702469</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>2.264465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>-2.165628</td>\n",
       "      <td>4.13145</td>\n",
       "      <td>0.580191</td>\n",
       "      <td>1.702469</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>2.854467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>-2.165628</td>\n",
       "      <td>4.13145</td>\n",
       "      <td>0.580191</td>\n",
       "      <td>1.702469</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2.863467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>-2.165628</td>\n",
       "      <td>4.13145</td>\n",
       "      <td>0.580191</td>\n",
       "      <td>1.702469</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             rt  response subj_idx my_condition         v        a         z  \\\n",
       "0      7.049264       0.0        0         high -0.356632  4.13145  0.580191   \n",
       "1      3.987486       0.0        0         high -0.356632  4.13145  0.580191   \n",
       "2     10.385347       1.0        0         high -0.356632  4.13145  0.580191   \n",
       "3      4.340460       0.0        0         high -0.356632  4.13145  0.580191   \n",
       "4      6.496305       0.0        0         high -0.356632  4.13145  0.580191   \n",
       "...         ...       ...      ...          ...       ...      ...       ...   \n",
       "1995   2.494462       0.0        0          low -2.165628  4.13145  0.580191   \n",
       "1996   2.534462       0.0        0          low -2.165628  4.13145  0.580191   \n",
       "1997   2.264465       0.0        0          low -2.165628  4.13145  0.580191   \n",
       "1998   2.854467       0.0        0          low -2.165628  4.13145  0.580191   \n",
       "1999   2.863467       0.0        0          low -2.165628  4.13145  0.580191   \n",
       "\n",
       "             t  my_condition_reg  \n",
       "0     1.702469                 1  \n",
       "1     1.702469                 1  \n",
       "2     1.702469                 1  \n",
       "3     1.702469                 1  \n",
       "4     1.702469                 1  \n",
       "...        ...               ...  \n",
       "1995  1.702469                 0  \n",
       "1996  1.702469                 0  \n",
       "1997  1.702469                 0  \n",
       "1998  1.702469                 0  \n",
       "1999  1.702469                 0  \n",
       "\n",
       "[2000 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simulate some data\n",
    "model = \"ddm_hddm_base\"\n",
    "n_samples = 1000\n",
    "\n",
    "data, parameter_dict = simulator_h_c(\n",
    "    n_subjects=1,\n",
    "    n_trials_per_subject=n_samples,\n",
    "    model=model,\n",
    "    p_outlier=0.00,\n",
    "    conditions={\"my_condition\": [\"high\", \"low\"]},\n",
    "    depends_on={\"v\": [\"my_condition\"]},\n",
    "    regression_models=None,\n",
    "    regression_covariates=None,\n",
    "    group_only_regressors=False,\n",
    "    group_only=None,\n",
    "    fixed_at_default=None,\n",
    ")\n",
    "\n",
    "data_new = deepcopy(data)\n",
    "data_new[\"my_condition_reg\"] = 0\n",
    "data_new.loc[data_new[\"my_condition\"] == \"high\", \"my_condition_reg\"] = 1\n",
    "\n",
    "data_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aafa30b-ad4b-42fe-a22b-df64dc282ff0",
   "metadata": {},
   "source": [
    "##### Using `HDDM()` and `depends_on`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c9664ba2-ce27-4c5e-ac7f-da2f31228e56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 69.8 sec"
     ]
    }
   ],
   "source": [
    "hddm_models_condition = []\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_condition.append(\n",
    "        hddm.HDDM(\n",
    "            data_new,\n",
    "            informative=True,\n",
    "            is_group_model=False,\n",
    "            depends_on={\"v\": \"my_condition\"},\n",
    "            include=[\"v\", \"a\", \"z\", \"t\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 2000\n",
    "for i in range(1):\n",
    "    print(i)\n",
    "    hddm_models_condition[i].sample(nmcmc, burn=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "577a0529-0c84-4b2f-b3d5-fa7ee07aad1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>4.113655</td>\n",
       "      <td>0.060311</td>\n",
       "      <td>4.007043</td>\n",
       "      <td>4.073393</td>\n",
       "      <td>4.110084</td>\n",
       "      <td>4.153248</td>\n",
       "      <td>4.238106</td>\n",
       "      <td>0.003601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v(high)</th>\n",
       "      <td>-0.32913</td>\n",
       "      <td>0.020014</td>\n",
       "      <td>-0.367812</td>\n",
       "      <td>-0.342413</td>\n",
       "      <td>-0.329411</td>\n",
       "      <td>-0.316262</td>\n",
       "      <td>-0.289232</td>\n",
       "      <td>0.000989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v(low)</th>\n",
       "      <td>-2.137678</td>\n",
       "      <td>0.044588</td>\n",
       "      <td>-2.227869</td>\n",
       "      <td>-2.165996</td>\n",
       "      <td>-2.136578</td>\n",
       "      <td>-2.108074</td>\n",
       "      <td>-2.051395</td>\n",
       "      <td>0.002409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>1.721661</td>\n",
       "      <td>0.018831</td>\n",
       "      <td>1.685962</td>\n",
       "      <td>1.707596</td>\n",
       "      <td>1.722271</td>\n",
       "      <td>1.73501</td>\n",
       "      <td>1.758521</td>\n",
       "      <td>0.001325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.558375</td>\n",
       "      <td>0.010421</td>\n",
       "      <td>0.537503</td>\n",
       "      <td>0.551727</td>\n",
       "      <td>0.558491</td>\n",
       "      <td>0.565858</td>\n",
       "      <td>0.578013</td>\n",
       "      <td>0.000683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean       std      2.5q       25q       50q       75q     97.5q  \\\n",
       "a        4.113655  0.060311  4.007043  4.073393  4.110084  4.153248  4.238106   \n",
       "v(high)  -0.32913  0.020014 -0.367812 -0.342413 -0.329411 -0.316262 -0.289232   \n",
       "v(low)  -2.137678  0.044588 -2.227869 -2.165996 -2.136578 -2.108074 -2.051395   \n",
       "t        1.721661  0.018831  1.685962  1.707596  1.722271   1.73501  1.758521   \n",
       "z        0.558375  0.010421  0.537503  0.551727  0.558491  0.565858  0.578013   \n",
       "\n",
       "           mc err  \n",
       "a        0.003601  \n",
       "v(high)  0.000989  \n",
       "v(low)   0.002409  \n",
       "t        0.001325  \n",
       "z        0.000683  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_condition[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ba1849b-b6dd-42e9-b79b-f3d61526d10f",
   "metadata": {},
   "source": [
    "##### Using `HDDMRegressor()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b6cab120-50aa-459f-8413-637f6139721b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 138.7 sec1\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 114.1 sec"
     ]
    }
   ],
   "source": [
    "hddm_models_reg = []\n",
    "\n",
    "\n",
    "def identity_link(x):\n",
    "    return x\n",
    "\n",
    "\n",
    "reg_models = [{\"model\": \"v ~ 1 + my_condition_reg\", \"link_func\": identity_link}]\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_reg.append(\n",
    "        hddm.HDDMRegressor(\n",
    "            data_new,\n",
    "            reg_models,\n",
    "            informative=True,\n",
    "            is_group_model=False,\n",
    "            include=[\"v\", \"a\", \"t\", \"z\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 2000\n",
    "for i in range(n_models):\n",
    "    print(i)\n",
    "    hddm_models_reg[i].sample(nmcmc, burn=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "97a9a9e5-3ef9-411c-a6e2-1cd07c6eb2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>4.117542</td>\n",
       "      <td>0.062469</td>\n",
       "      <td>3.991596</td>\n",
       "      <td>4.079858</td>\n",
       "      <td>4.119159</td>\n",
       "      <td>4.157671</td>\n",
       "      <td>4.23758</td>\n",
       "      <td>0.00354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>1.718996</td>\n",
       "      <td>0.019105</td>\n",
       "      <td>1.681989</td>\n",
       "      <td>1.705641</td>\n",
       "      <td>1.718617</td>\n",
       "      <td>1.73227</td>\n",
       "      <td>1.75525</td>\n",
       "      <td>0.001244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.559359</td>\n",
       "      <td>0.010333</td>\n",
       "      <td>0.538583</td>\n",
       "      <td>0.552534</td>\n",
       "      <td>0.559771</td>\n",
       "      <td>0.566213</td>\n",
       "      <td>0.579105</td>\n",
       "      <td>0.000709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept</th>\n",
       "      <td>-2.137995</td>\n",
       "      <td>0.046484</td>\n",
       "      <td>-2.226494</td>\n",
       "      <td>-2.170064</td>\n",
       "      <td>-2.138143</td>\n",
       "      <td>-2.10573</td>\n",
       "      <td>-2.04313</td>\n",
       "      <td>0.003936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg</th>\n",
       "      <td>1.807836</td>\n",
       "      <td>0.044491</td>\n",
       "      <td>1.716766</td>\n",
       "      <td>1.778369</td>\n",
       "      <td>1.80801</td>\n",
       "      <td>1.838655</td>\n",
       "      <td>1.892908</td>\n",
       "      <td>0.003667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mean       std      2.5q       25q       50q  \\\n",
       "a                   4.117542  0.062469  3.991596  4.079858  4.119159   \n",
       "t                   1.718996  0.019105  1.681989  1.705641  1.718617   \n",
       "z                   0.559359  0.010333  0.538583  0.552534  0.559771   \n",
       "v_Intercept        -2.137995  0.046484 -2.226494 -2.170064 -2.138143   \n",
       "v_my_condition_reg  1.807836  0.044491  1.716766  1.778369   1.80801   \n",
       "\n",
       "                         75q     97.5q    mc err  \n",
       "a                   4.157671   4.23758   0.00354  \n",
       "t                    1.73227   1.75525  0.001244  \n",
       "z                   0.566213  0.579105  0.000709  \n",
       "v_Intercept         -2.10573  -2.04313  0.003936  \n",
       "v_my_condition_reg  1.838655  1.892908  0.003667  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_reg[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622e115d-b50a-411d-bd9b-5cf047610262",
   "metadata": {},
   "source": [
    "#### Condition / Regression, hierarchical\n",
    "\n",
    "Replicating the analysis above, but this time using a hierarchical model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bfdef899-2ecf-4ad9-b83a-6449286b7aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate some data\n",
    "model = \"ddm_hddm_base\"\n",
    "n_samples = 200\n",
    "\n",
    "data, parameter_dict = simulator_h_c(\n",
    "    n_subjects=10,\n",
    "    n_trials_per_subject=n_samples,\n",
    "    model=model,\n",
    "    p_outlier=0.00,\n",
    "    conditions={\"my_condition\": [\"high\", \"low\"]},\n",
    "    depends_on={\"v\": [\"my_condition\"]},\n",
    "    regression_models=None,\n",
    "    regression_covariates=None,\n",
    "    group_only_regressors=False,\n",
    "    group_only=None,\n",
    "    fixed_at_default=None,\n",
    ")\n",
    "\n",
    "data_new\n",
    "data_new = deepcopy(data)\n",
    "\n",
    "data_new[\"my_condition_reg\"] = 0\n",
    "data_new.loc[data_new[\"my_condition\"] == \"high\", \"my_condition_reg\"] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd44d83-1ecd-43dc-baed-203e59bf2b9a",
   "metadata": {},
   "source": [
    "##### Using `HDDM()` and `depends_on`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8d76aa42-b1f1-4ce0-a342-87016cd0f98b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 404.5 sec"
     ]
    }
   ],
   "source": [
    "# Define the HDDM model\n",
    "hddm_models_group_condition = []\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_group_condition.append(\n",
    "        hddm.HDDM(\n",
    "            data_new,\n",
    "            informative=True,\n",
    "            is_group_model=True,\n",
    "            depends_on={\"v\": \"my_condition\"},\n",
    "            include=[\"v\", \"a\", \"z\", \"t\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 2000\n",
    "for i in range(1):\n",
    "    print(i)\n",
    "    hddm_models_group_condition[i].sample(nmcmc, burn=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "23bb2813-072b-4e9a-947b-f6ff08043fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.992322</td>\n",
       "      <td>0.162465</td>\n",
       "      <td>0.726933</td>\n",
       "      <td>0.888133</td>\n",
       "      <td>0.969025</td>\n",
       "      <td>1.081929</td>\n",
       "      <td>1.356581</td>\n",
       "      <td>0.00892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_std</th>\n",
       "      <td>0.479292</td>\n",
       "      <td>0.175739</td>\n",
       "      <td>0.264594</td>\n",
       "      <td>0.359975</td>\n",
       "      <td>0.440031</td>\n",
       "      <td>0.544133</td>\n",
       "      <td>0.987413</td>\n",
       "      <td>0.00987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.0</th>\n",
       "      <td>0.754126</td>\n",
       "      <td>0.019615</td>\n",
       "      <td>0.71519</td>\n",
       "      <td>0.741662</td>\n",
       "      <td>0.752973</td>\n",
       "      <td>0.767971</td>\n",
       "      <td>0.793827</td>\n",
       "      <td>0.000821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.1</th>\n",
       "      <td>1.187689</td>\n",
       "      <td>0.030408</td>\n",
       "      <td>1.127662</td>\n",
       "      <td>1.165985</td>\n",
       "      <td>1.18894</td>\n",
       "      <td>1.208663</td>\n",
       "      <td>1.249407</td>\n",
       "      <td>0.001342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.2</th>\n",
       "      <td>0.986146</td>\n",
       "      <td>0.028002</td>\n",
       "      <td>0.929371</td>\n",
       "      <td>0.967481</td>\n",
       "      <td>0.986614</td>\n",
       "      <td>1.005247</td>\n",
       "      <td>1.039542</td>\n",
       "      <td>0.001306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.3</th>\n",
       "      <td>0.849772</td>\n",
       "      <td>0.022941</td>\n",
       "      <td>0.807387</td>\n",
       "      <td>0.833865</td>\n",
       "      <td>0.848167</td>\n",
       "      <td>0.866022</td>\n",
       "      <td>0.895378</td>\n",
       "      <td>0.001013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.4</th>\n",
       "      <td>0.852587</td>\n",
       "      <td>0.022515</td>\n",
       "      <td>0.810993</td>\n",
       "      <td>0.836551</td>\n",
       "      <td>0.852403</td>\n",
       "      <td>0.867418</td>\n",
       "      <td>0.89827</td>\n",
       "      <td>0.000936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.5</th>\n",
       "      <td>0.255972</td>\n",
       "      <td>0.006265</td>\n",
       "      <td>0.243915</td>\n",
       "      <td>0.251871</td>\n",
       "      <td>0.256134</td>\n",
       "      <td>0.260136</td>\n",
       "      <td>0.268621</td>\n",
       "      <td>0.000249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.6</th>\n",
       "      <td>0.861594</td>\n",
       "      <td>0.024355</td>\n",
       "      <td>0.814351</td>\n",
       "      <td>0.845551</td>\n",
       "      <td>0.861699</td>\n",
       "      <td>0.877971</td>\n",
       "      <td>0.910797</td>\n",
       "      <td>0.001159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.7</th>\n",
       "      <td>0.869047</td>\n",
       "      <td>0.021844</td>\n",
       "      <td>0.828341</td>\n",
       "      <td>0.854532</td>\n",
       "      <td>0.86734</td>\n",
       "      <td>0.883431</td>\n",
       "      <td>0.914712</td>\n",
       "      <td>0.000953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.8</th>\n",
       "      <td>0.936377</td>\n",
       "      <td>0.025632</td>\n",
       "      <td>0.889594</td>\n",
       "      <td>0.918273</td>\n",
       "      <td>0.934346</td>\n",
       "      <td>0.952847</td>\n",
       "      <td>0.990287</td>\n",
       "      <td>0.001162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.9</th>\n",
       "      <td>1.502509</td>\n",
       "      <td>0.043942</td>\n",
       "      <td>1.415737</td>\n",
       "      <td>1.472341</td>\n",
       "      <td>1.501</td>\n",
       "      <td>1.532253</td>\n",
       "      <td>1.590165</td>\n",
       "      <td>0.002117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v(high)</th>\n",
       "      <td>0.037347</td>\n",
       "      <td>0.11003</td>\n",
       "      <td>-0.182164</td>\n",
       "      <td>-0.033148</td>\n",
       "      <td>0.034653</td>\n",
       "      <td>0.102257</td>\n",
       "      <td>0.279009</td>\n",
       "      <td>0.005197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v(low)</th>\n",
       "      <td>-2.604585</td>\n",
       "      <td>0.119718</td>\n",
       "      <td>-2.841027</td>\n",
       "      <td>-2.682751</td>\n",
       "      <td>-2.610824</td>\n",
       "      <td>-2.524737</td>\n",
       "      <td>-2.36562</td>\n",
       "      <td>0.005828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_std</th>\n",
       "      <td>0.267058</td>\n",
       "      <td>0.078649</td>\n",
       "      <td>0.14111</td>\n",
       "      <td>0.2122</td>\n",
       "      <td>0.255867</td>\n",
       "      <td>0.312991</td>\n",
       "      <td>0.458069</td>\n",
       "      <td>0.005108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(high).0</th>\n",
       "      <td>0.027134</td>\n",
       "      <td>0.168097</td>\n",
       "      <td>-0.337042</td>\n",
       "      <td>-0.077507</td>\n",
       "      <td>0.030914</td>\n",
       "      <td>0.130327</td>\n",
       "      <td>0.358344</td>\n",
       "      <td>0.007198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(high).1</th>\n",
       "      <td>0.30762</td>\n",
       "      <td>0.125728</td>\n",
       "      <td>0.041653</td>\n",
       "      <td>0.225623</td>\n",
       "      <td>0.309572</td>\n",
       "      <td>0.38934</td>\n",
       "      <td>0.553225</td>\n",
       "      <td>0.005815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(high).2</th>\n",
       "      <td>0.032505</td>\n",
       "      <td>0.147922</td>\n",
       "      <td>-0.255638</td>\n",
       "      <td>-0.062011</td>\n",
       "      <td>0.031879</td>\n",
       "      <td>0.128816</td>\n",
       "      <td>0.333984</td>\n",
       "      <td>0.005932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(high).3</th>\n",
       "      <td>-0.2021</td>\n",
       "      <td>0.168979</td>\n",
       "      <td>-0.547075</td>\n",
       "      <td>-0.310606</td>\n",
       "      <td>-0.196799</td>\n",
       "      <td>-0.084638</td>\n",
       "      <td>0.11976</td>\n",
       "      <td>0.00727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(high).4</th>\n",
       "      <td>0.01671</td>\n",
       "      <td>0.147662</td>\n",
       "      <td>-0.267713</td>\n",
       "      <td>-0.083319</td>\n",
       "      <td>0.017837</td>\n",
       "      <td>0.119273</td>\n",
       "      <td>0.313308</td>\n",
       "      <td>0.005991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(high).5</th>\n",
       "      <td>0.242472</td>\n",
       "      <td>0.275164</td>\n",
       "      <td>-0.234032</td>\n",
       "      <td>0.055175</td>\n",
       "      <td>0.216841</td>\n",
       "      <td>0.412081</td>\n",
       "      <td>0.892688</td>\n",
       "      <td>0.013376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(high).6</th>\n",
       "      <td>0.135152</td>\n",
       "      <td>0.152953</td>\n",
       "      <td>-0.178299</td>\n",
       "      <td>0.027532</td>\n",
       "      <td>0.143217</td>\n",
       "      <td>0.238922</td>\n",
       "      <td>0.438099</td>\n",
       "      <td>0.005517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(high).7</th>\n",
       "      <td>0.060871</td>\n",
       "      <td>0.150594</td>\n",
       "      <td>-0.251811</td>\n",
       "      <td>-0.035252</td>\n",
       "      <td>0.055472</td>\n",
       "      <td>0.160798</td>\n",
       "      <td>0.357507</td>\n",
       "      <td>0.006149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(high).8</th>\n",
       "      <td>-0.008495</td>\n",
       "      <td>0.153157</td>\n",
       "      <td>-0.309212</td>\n",
       "      <td>-0.10699</td>\n",
       "      <td>-0.006054</td>\n",
       "      <td>0.090268</td>\n",
       "      <td>0.314571</td>\n",
       "      <td>0.005828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(high).9</th>\n",
       "      <td>-0.196465</td>\n",
       "      <td>0.100561</td>\n",
       "      <td>-0.394228</td>\n",
       "      <td>-0.264959</td>\n",
       "      <td>-0.197081</td>\n",
       "      <td>-0.123822</td>\n",
       "      <td>0.006427</td>\n",
       "      <td>0.004025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(low).0</th>\n",
       "      <td>-2.582833</td>\n",
       "      <td>0.186556</td>\n",
       "      <td>-2.963151</td>\n",
       "      <td>-2.699676</td>\n",
       "      <td>-2.585623</td>\n",
       "      <td>-2.462659</td>\n",
       "      <td>-2.210883</td>\n",
       "      <td>0.007109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(low).1</th>\n",
       "      <td>-2.604226</td>\n",
       "      <td>0.158169</td>\n",
       "      <td>-2.9167</td>\n",
       "      <td>-2.706663</td>\n",
       "      <td>-2.608595</td>\n",
       "      <td>-2.500787</td>\n",
       "      <td>-2.279551</td>\n",
       "      <td>0.006865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(low).2</th>\n",
       "      <td>-2.695816</td>\n",
       "      <td>0.173007</td>\n",
       "      <td>-3.031333</td>\n",
       "      <td>-2.806813</td>\n",
       "      <td>-2.698342</td>\n",
       "      <td>-2.581273</td>\n",
       "      <td>-2.345902</td>\n",
       "      <td>0.007186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(low).3</th>\n",
       "      <td>-2.575839</td>\n",
       "      <td>0.182447</td>\n",
       "      <td>-2.920043</td>\n",
       "      <td>-2.697408</td>\n",
       "      <td>-2.574668</td>\n",
       "      <td>-2.453435</td>\n",
       "      <td>-2.228805</td>\n",
       "      <td>0.007303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(low).4</th>\n",
       "      <td>-2.737749</td>\n",
       "      <td>0.203466</td>\n",
       "      <td>-3.159003</td>\n",
       "      <td>-2.865159</td>\n",
       "      <td>-2.725582</td>\n",
       "      <td>-2.599216</td>\n",
       "      <td>-2.354871</td>\n",
       "      <td>0.008097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(low).5</th>\n",
       "      <td>-2.560398</td>\n",
       "      <td>0.268496</td>\n",
       "      <td>-3.049226</td>\n",
       "      <td>-2.731729</td>\n",
       "      <td>-2.563273</td>\n",
       "      <td>-2.395627</td>\n",
       "      <td>-2.00906</td>\n",
       "      <td>0.010754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(low).6</th>\n",
       "      <td>-2.906562</td>\n",
       "      <td>0.204087</td>\n",
       "      <td>-3.314842</td>\n",
       "      <td>-3.041678</td>\n",
       "      <td>-2.895627</td>\n",
       "      <td>-2.772911</td>\n",
       "      <td>-2.527107</td>\n",
       "      <td>0.008968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(low).7</th>\n",
       "      <td>-2.831635</td>\n",
       "      <td>0.194387</td>\n",
       "      <td>-3.232384</td>\n",
       "      <td>-2.956886</td>\n",
       "      <td>-2.819781</td>\n",
       "      <td>-2.701759</td>\n",
       "      <td>-2.458215</td>\n",
       "      <td>0.008923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(low).8</th>\n",
       "      <td>-2.374748</td>\n",
       "      <td>0.175835</td>\n",
       "      <td>-2.692479</td>\n",
       "      <td>-2.494457</td>\n",
       "      <td>-2.382132</td>\n",
       "      <td>-2.264681</td>\n",
       "      <td>-2.012672</td>\n",
       "      <td>0.007641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj(low).9</th>\n",
       "      <td>-2.220735</td>\n",
       "      <td>0.148448</td>\n",
       "      <td>-2.509302</td>\n",
       "      <td>-2.322084</td>\n",
       "      <td>-2.220226</td>\n",
       "      <td>-2.124121</td>\n",
       "      <td>-1.916862</td>\n",
       "      <td>0.007332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>1.766362</td>\n",
       "      <td>0.039485</td>\n",
       "      <td>1.683088</td>\n",
       "      <td>1.73989</td>\n",
       "      <td>1.766421</td>\n",
       "      <td>1.790686</td>\n",
       "      <td>1.845539</td>\n",
       "      <td>0.0013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_std</th>\n",
       "      <td>0.12734</td>\n",
       "      <td>0.035304</td>\n",
       "      <td>0.077163</td>\n",
       "      <td>0.10256</td>\n",
       "      <td>0.120733</td>\n",
       "      <td>0.14493</td>\n",
       "      <td>0.218545</td>\n",
       "      <td>0.001775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.0</th>\n",
       "      <td>1.729981</td>\n",
       "      <td>0.001528</td>\n",
       "      <td>1.726802</td>\n",
       "      <td>1.728988</td>\n",
       "      <td>1.730077</td>\n",
       "      <td>1.731074</td>\n",
       "      <td>1.732731</td>\n",
       "      <td>0.000075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.1</th>\n",
       "      <td>1.876523</td>\n",
       "      <td>0.003503</td>\n",
       "      <td>1.8693</td>\n",
       "      <td>1.874348</td>\n",
       "      <td>1.876607</td>\n",
       "      <td>1.878996</td>\n",
       "      <td>1.88271</td>\n",
       "      <td>0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.2</th>\n",
       "      <td>1.639014</td>\n",
       "      <td>0.002042</td>\n",
       "      <td>1.634604</td>\n",
       "      <td>1.637619</td>\n",
       "      <td>1.639226</td>\n",
       "      <td>1.640499</td>\n",
       "      <td>1.642571</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.3</th>\n",
       "      <td>1.739884</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>1.736336</td>\n",
       "      <td>1.738888</td>\n",
       "      <td>1.740017</td>\n",
       "      <td>1.741025</td>\n",
       "      <td>1.7427</td>\n",
       "      <td>0.000081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.4</th>\n",
       "      <td>1.82303</td>\n",
       "      <td>0.001972</td>\n",
       "      <td>1.818791</td>\n",
       "      <td>1.821729</td>\n",
       "      <td>1.823215</td>\n",
       "      <td>1.82445</td>\n",
       "      <td>1.826447</td>\n",
       "      <td>0.00011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.5</th>\n",
       "      <td>1.780351</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>1.779905</td>\n",
       "      <td>1.780215</td>\n",
       "      <td>1.780366</td>\n",
       "      <td>1.780499</td>\n",
       "      <td>1.780726</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.6</th>\n",
       "      <td>1.773232</td>\n",
       "      <td>0.002221</td>\n",
       "      <td>1.768536</td>\n",
       "      <td>1.771848</td>\n",
       "      <td>1.773294</td>\n",
       "      <td>1.7748</td>\n",
       "      <td>1.777251</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.7</th>\n",
       "      <td>1.595907</td>\n",
       "      <td>0.001848</td>\n",
       "      <td>1.592028</td>\n",
       "      <td>1.594729</td>\n",
       "      <td>1.596118</td>\n",
       "      <td>1.597215</td>\n",
       "      <td>1.599034</td>\n",
       "      <td>0.000081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.8</th>\n",
       "      <td>1.846612</td>\n",
       "      <td>0.002454</td>\n",
       "      <td>1.84154</td>\n",
       "      <td>1.845044</td>\n",
       "      <td>1.846832</td>\n",
       "      <td>1.848407</td>\n",
       "      <td>1.851016</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.9</th>\n",
       "      <td>1.955246</td>\n",
       "      <td>0.006152</td>\n",
       "      <td>1.942041</td>\n",
       "      <td>1.951332</td>\n",
       "      <td>1.955471</td>\n",
       "      <td>1.959747</td>\n",
       "      <td>1.966342</td>\n",
       "      <td>0.000344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.407807</td>\n",
       "      <td>0.010752</td>\n",
       "      <td>0.386492</td>\n",
       "      <td>0.400437</td>\n",
       "      <td>0.407658</td>\n",
       "      <td>0.414985</td>\n",
       "      <td>0.428708</td>\n",
       "      <td>0.000519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_std</th>\n",
       "      <td>0.116055</td>\n",
       "      <td>0.024083</td>\n",
       "      <td>0.074094</td>\n",
       "      <td>0.09875</td>\n",
       "      <td>0.114891</td>\n",
       "      <td>0.130756</td>\n",
       "      <td>0.166337</td>\n",
       "      <td>0.001286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.0</th>\n",
       "      <td>0.406017</td>\n",
       "      <td>0.014879</td>\n",
       "      <td>0.375714</td>\n",
       "      <td>0.396134</td>\n",
       "      <td>0.406485</td>\n",
       "      <td>0.415876</td>\n",
       "      <td>0.436113</td>\n",
       "      <td>0.000738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.1</th>\n",
       "      <td>0.429711</td>\n",
       "      <td>0.015678</td>\n",
       "      <td>0.399125</td>\n",
       "      <td>0.418875</td>\n",
       "      <td>0.429377</td>\n",
       "      <td>0.439964</td>\n",
       "      <td>0.462085</td>\n",
       "      <td>0.000743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.2</th>\n",
       "      <td>0.352977</td>\n",
       "      <td>0.01702</td>\n",
       "      <td>0.320604</td>\n",
       "      <td>0.340873</td>\n",
       "      <td>0.352495</td>\n",
       "      <td>0.365135</td>\n",
       "      <td>0.386391</td>\n",
       "      <td>0.001046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.3</th>\n",
       "      <td>0.375455</td>\n",
       "      <td>0.014801</td>\n",
       "      <td>0.346944</td>\n",
       "      <td>0.365888</td>\n",
       "      <td>0.374982</td>\n",
       "      <td>0.385191</td>\n",
       "      <td>0.404263</td>\n",
       "      <td>0.000785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.4</th>\n",
       "      <td>0.38218</td>\n",
       "      <td>0.015622</td>\n",
       "      <td>0.353119</td>\n",
       "      <td>0.371124</td>\n",
       "      <td>0.382321</td>\n",
       "      <td>0.392112</td>\n",
       "      <td>0.413962</td>\n",
       "      <td>0.000853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.5</th>\n",
       "      <td>0.453517</td>\n",
       "      <td>0.01383</td>\n",
       "      <td>0.425974</td>\n",
       "      <td>0.444724</td>\n",
       "      <td>0.453692</td>\n",
       "      <td>0.462794</td>\n",
       "      <td>0.480666</td>\n",
       "      <td>0.000582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.6</th>\n",
       "      <td>0.426052</td>\n",
       "      <td>0.014763</td>\n",
       "      <td>0.399671</td>\n",
       "      <td>0.415677</td>\n",
       "      <td>0.42503</td>\n",
       "      <td>0.435699</td>\n",
       "      <td>0.457784</td>\n",
       "      <td>0.000613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.7</th>\n",
       "      <td>0.410265</td>\n",
       "      <td>0.014982</td>\n",
       "      <td>0.383095</td>\n",
       "      <td>0.399993</td>\n",
       "      <td>0.41016</td>\n",
       "      <td>0.419582</td>\n",
       "      <td>0.442261</td>\n",
       "      <td>0.000692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.8</th>\n",
       "      <td>0.430641</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.401914</td>\n",
       "      <td>0.420067</td>\n",
       "      <td>0.42957</td>\n",
       "      <td>0.441181</td>\n",
       "      <td>0.462136</td>\n",
       "      <td>0.000736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.9</th>\n",
       "      <td>0.41068</td>\n",
       "      <td>0.016752</td>\n",
       "      <td>0.380886</td>\n",
       "      <td>0.398537</td>\n",
       "      <td>0.409705</td>\n",
       "      <td>0.421619</td>\n",
       "      <td>0.444578</td>\n",
       "      <td>0.000961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean       std      2.5q       25q       50q       75q  \\\n",
       "a               0.992322  0.162465  0.726933  0.888133  0.969025  1.081929   \n",
       "a_std           0.479292  0.175739  0.264594  0.359975  0.440031  0.544133   \n",
       "a_subj.0        0.754126  0.019615   0.71519  0.741662  0.752973  0.767971   \n",
       "a_subj.1        1.187689  0.030408  1.127662  1.165985   1.18894  1.208663   \n",
       "a_subj.2        0.986146  0.028002  0.929371  0.967481  0.986614  1.005247   \n",
       "a_subj.3        0.849772  0.022941  0.807387  0.833865  0.848167  0.866022   \n",
       "a_subj.4        0.852587  0.022515  0.810993  0.836551  0.852403  0.867418   \n",
       "a_subj.5        0.255972  0.006265  0.243915  0.251871  0.256134  0.260136   \n",
       "a_subj.6        0.861594  0.024355  0.814351  0.845551  0.861699  0.877971   \n",
       "a_subj.7        0.869047  0.021844  0.828341  0.854532   0.86734  0.883431   \n",
       "a_subj.8        0.936377  0.025632  0.889594  0.918273  0.934346  0.952847   \n",
       "a_subj.9        1.502509  0.043942  1.415737  1.472341     1.501  1.532253   \n",
       "v(high)         0.037347   0.11003 -0.182164 -0.033148  0.034653  0.102257   \n",
       "v(low)         -2.604585  0.119718 -2.841027 -2.682751 -2.610824 -2.524737   \n",
       "v_std           0.267058  0.078649   0.14111    0.2122  0.255867  0.312991   \n",
       "v_subj(high).0  0.027134  0.168097 -0.337042 -0.077507  0.030914  0.130327   \n",
       "v_subj(high).1   0.30762  0.125728  0.041653  0.225623  0.309572   0.38934   \n",
       "v_subj(high).2  0.032505  0.147922 -0.255638 -0.062011  0.031879  0.128816   \n",
       "v_subj(high).3   -0.2021  0.168979 -0.547075 -0.310606 -0.196799 -0.084638   \n",
       "v_subj(high).4   0.01671  0.147662 -0.267713 -0.083319  0.017837  0.119273   \n",
       "v_subj(high).5  0.242472  0.275164 -0.234032  0.055175  0.216841  0.412081   \n",
       "v_subj(high).6  0.135152  0.152953 -0.178299  0.027532  0.143217  0.238922   \n",
       "v_subj(high).7  0.060871  0.150594 -0.251811 -0.035252  0.055472  0.160798   \n",
       "v_subj(high).8 -0.008495  0.153157 -0.309212  -0.10699 -0.006054  0.090268   \n",
       "v_subj(high).9 -0.196465  0.100561 -0.394228 -0.264959 -0.197081 -0.123822   \n",
       "v_subj(low).0  -2.582833  0.186556 -2.963151 -2.699676 -2.585623 -2.462659   \n",
       "v_subj(low).1  -2.604226  0.158169   -2.9167 -2.706663 -2.608595 -2.500787   \n",
       "v_subj(low).2  -2.695816  0.173007 -3.031333 -2.806813 -2.698342 -2.581273   \n",
       "v_subj(low).3  -2.575839  0.182447 -2.920043 -2.697408 -2.574668 -2.453435   \n",
       "v_subj(low).4  -2.737749  0.203466 -3.159003 -2.865159 -2.725582 -2.599216   \n",
       "v_subj(low).5  -2.560398  0.268496 -3.049226 -2.731729 -2.563273 -2.395627   \n",
       "v_subj(low).6  -2.906562  0.204087 -3.314842 -3.041678 -2.895627 -2.772911   \n",
       "v_subj(low).7  -2.831635  0.194387 -3.232384 -2.956886 -2.819781 -2.701759   \n",
       "v_subj(low).8  -2.374748  0.175835 -2.692479 -2.494457 -2.382132 -2.264681   \n",
       "v_subj(low).9  -2.220735  0.148448 -2.509302 -2.322084 -2.220226 -2.124121   \n",
       "t               1.766362  0.039485  1.683088   1.73989  1.766421  1.790686   \n",
       "t_std            0.12734  0.035304  0.077163   0.10256  0.120733   0.14493   \n",
       "t_subj.0        1.729981  0.001528  1.726802  1.728988  1.730077  1.731074   \n",
       "t_subj.1        1.876523  0.003503    1.8693  1.874348  1.876607  1.878996   \n",
       "t_subj.2        1.639014  0.002042  1.634604  1.637619  1.639226  1.640499   \n",
       "t_subj.3        1.739884  0.001627  1.736336  1.738888  1.740017  1.741025   \n",
       "t_subj.4         1.82303  0.001972  1.818791  1.821729  1.823215   1.82445   \n",
       "t_subj.5        1.780351  0.000209  1.779905  1.780215  1.780366  1.780499   \n",
       "t_subj.6        1.773232  0.002221  1.768536  1.771848  1.773294    1.7748   \n",
       "t_subj.7        1.595907  0.001848  1.592028  1.594729  1.596118  1.597215   \n",
       "t_subj.8        1.846612  0.002454   1.84154  1.845044  1.846832  1.848407   \n",
       "t_subj.9        1.955246  0.006152  1.942041  1.951332  1.955471  1.959747   \n",
       "z               0.407807  0.010752  0.386492  0.400437  0.407658  0.414985   \n",
       "z_std           0.116055  0.024083  0.074094   0.09875  0.114891  0.130756   \n",
       "z_subj.0        0.406017  0.014879  0.375714  0.396134  0.406485  0.415876   \n",
       "z_subj.1        0.429711  0.015678  0.399125  0.418875  0.429377  0.439964   \n",
       "z_subj.2        0.352977   0.01702  0.320604  0.340873  0.352495  0.365135   \n",
       "z_subj.3        0.375455  0.014801  0.346944  0.365888  0.374982  0.385191   \n",
       "z_subj.4         0.38218  0.015622  0.353119  0.371124  0.382321  0.392112   \n",
       "z_subj.5        0.453517   0.01383  0.425974  0.444724  0.453692  0.462794   \n",
       "z_subj.6        0.426052  0.014763  0.399671  0.415677   0.42503  0.435699   \n",
       "z_subj.7        0.410265  0.014982  0.383095  0.399993   0.41016  0.419582   \n",
       "z_subj.8        0.430641  0.015389  0.401914  0.420067   0.42957  0.441181   \n",
       "z_subj.9         0.41068  0.016752  0.380886  0.398537  0.409705  0.421619   \n",
       "\n",
       "                   97.5q    mc err  \n",
       "a               1.356581   0.00892  \n",
       "a_std           0.987413   0.00987  \n",
       "a_subj.0        0.793827  0.000821  \n",
       "a_subj.1        1.249407  0.001342  \n",
       "a_subj.2        1.039542  0.001306  \n",
       "a_subj.3        0.895378  0.001013  \n",
       "a_subj.4         0.89827  0.000936  \n",
       "a_subj.5        0.268621  0.000249  \n",
       "a_subj.6        0.910797  0.001159  \n",
       "a_subj.7        0.914712  0.000953  \n",
       "a_subj.8        0.990287  0.001162  \n",
       "a_subj.9        1.590165  0.002117  \n",
       "v(high)         0.279009  0.005197  \n",
       "v(low)          -2.36562  0.005828  \n",
       "v_std           0.458069  0.005108  \n",
       "v_subj(high).0  0.358344  0.007198  \n",
       "v_subj(high).1  0.553225  0.005815  \n",
       "v_subj(high).2  0.333984  0.005932  \n",
       "v_subj(high).3   0.11976   0.00727  \n",
       "v_subj(high).4  0.313308  0.005991  \n",
       "v_subj(high).5  0.892688  0.013376  \n",
       "v_subj(high).6  0.438099  0.005517  \n",
       "v_subj(high).7  0.357507  0.006149  \n",
       "v_subj(high).8  0.314571  0.005828  \n",
       "v_subj(high).9  0.006427  0.004025  \n",
       "v_subj(low).0  -2.210883  0.007109  \n",
       "v_subj(low).1  -2.279551  0.006865  \n",
       "v_subj(low).2  -2.345902  0.007186  \n",
       "v_subj(low).3  -2.228805  0.007303  \n",
       "v_subj(low).4  -2.354871  0.008097  \n",
       "v_subj(low).5   -2.00906  0.010754  \n",
       "v_subj(low).6  -2.527107  0.008968  \n",
       "v_subj(low).7  -2.458215  0.008923  \n",
       "v_subj(low).8  -2.012672  0.007641  \n",
       "v_subj(low).9  -1.916862  0.007332  \n",
       "t               1.845539    0.0013  \n",
       "t_std           0.218545  0.001775  \n",
       "t_subj.0        1.732731  0.000075  \n",
       "t_subj.1         1.88271  0.000168  \n",
       "t_subj.2        1.642571  0.000108  \n",
       "t_subj.3          1.7427  0.000081  \n",
       "t_subj.4        1.826447   0.00011  \n",
       "t_subj.5        1.780726  0.000008  \n",
       "t_subj.6        1.777251  0.000106  \n",
       "t_subj.7        1.599034  0.000081  \n",
       "t_subj.8        1.851016  0.000134  \n",
       "t_subj.9        1.966342  0.000344  \n",
       "z               0.428708  0.000519  \n",
       "z_std           0.166337  0.001286  \n",
       "z_subj.0        0.436113  0.000738  \n",
       "z_subj.1        0.462085  0.000743  \n",
       "z_subj.2        0.386391  0.001046  \n",
       "z_subj.3        0.404263  0.000785  \n",
       "z_subj.4        0.413962  0.000853  \n",
       "z_subj.5        0.480666  0.000582  \n",
       "z_subj.6        0.457784  0.000613  \n",
       "z_subj.7        0.442261  0.000692  \n",
       "z_subj.8        0.462136  0.000736  \n",
       "z_subj.9        0.444578  0.000961  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_group_condition[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12bddd95-4381-45ea-95bd-d1bab2f701a7",
   "metadata": {},
   "source": [
    "##### Using `HDDMRegressor()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d307df92-d7a6-495e-8267-4e4abbe121e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 2001 of 2000 complete in 1097.9 sec1\n",
      " [-----------------100%-----------------] 2001 of 2000 complete in 816.4 sec"
     ]
    }
   ],
   "source": [
    "hddm_models_group_reg = []\n",
    "\n",
    "\n",
    "def identity_link(x):\n",
    "    return x\n",
    "\n",
    "\n",
    "reg_models = [{\"model\": \"v ~ 1 + my_condition_reg\", \"link_func\": identity_link}]\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_group_reg.append(\n",
    "        hddm.HDDMRegressor(\n",
    "            data_new,\n",
    "            reg_models,\n",
    "            informative=True,\n",
    "            is_group_model=True,\n",
    "            group_only_regressors=False,\n",
    "            include=[\"v\", \"a\", \"z\", \"t\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 2000\n",
    "for i in range(n_models):\n",
    "    print(i)\n",
    "    hddm_models_group_reg[i].sample(nmcmc, burn=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2aa44206-6c27-462c-9e41-ebdf45d6c951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.979899</td>\n",
       "      <td>0.157335</td>\n",
       "      <td>0.748584</td>\n",
       "      <td>0.876946</td>\n",
       "      <td>0.959024</td>\n",
       "      <td>1.054162</td>\n",
       "      <td>1.311815</td>\n",
       "      <td>0.008575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_std</th>\n",
       "      <td>0.46193</td>\n",
       "      <td>0.170778</td>\n",
       "      <td>0.253589</td>\n",
       "      <td>0.350913</td>\n",
       "      <td>0.423853</td>\n",
       "      <td>0.527927</td>\n",
       "      <td>0.930596</td>\n",
       "      <td>0.010485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.0</th>\n",
       "      <td>0.754792</td>\n",
       "      <td>0.019591</td>\n",
       "      <td>0.719274</td>\n",
       "      <td>0.74053</td>\n",
       "      <td>0.754511</td>\n",
       "      <td>0.767652</td>\n",
       "      <td>0.797013</td>\n",
       "      <td>0.000781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.1</th>\n",
       "      <td>1.186801</td>\n",
       "      <td>0.032068</td>\n",
       "      <td>1.127424</td>\n",
       "      <td>1.16474</td>\n",
       "      <td>1.185715</td>\n",
       "      <td>1.20769</td>\n",
       "      <td>1.252286</td>\n",
       "      <td>0.001459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.2</th>\n",
       "      <td>0.984794</td>\n",
       "      <td>0.027371</td>\n",
       "      <td>0.930539</td>\n",
       "      <td>0.966547</td>\n",
       "      <td>0.984786</td>\n",
       "      <td>1.004296</td>\n",
       "      <td>1.039688</td>\n",
       "      <td>0.001087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.3</th>\n",
       "      <td>0.852952</td>\n",
       "      <td>0.023745</td>\n",
       "      <td>0.807877</td>\n",
       "      <td>0.835383</td>\n",
       "      <td>0.854175</td>\n",
       "      <td>0.870471</td>\n",
       "      <td>0.896436</td>\n",
       "      <td>0.001074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.4</th>\n",
       "      <td>0.852153</td>\n",
       "      <td>0.022061</td>\n",
       "      <td>0.811592</td>\n",
       "      <td>0.836282</td>\n",
       "      <td>0.85154</td>\n",
       "      <td>0.867075</td>\n",
       "      <td>0.895638</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.5</th>\n",
       "      <td>0.255451</td>\n",
       "      <td>0.006012</td>\n",
       "      <td>0.244435</td>\n",
       "      <td>0.251348</td>\n",
       "      <td>0.255234</td>\n",
       "      <td>0.25918</td>\n",
       "      <td>0.268033</td>\n",
       "      <td>0.000256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.6</th>\n",
       "      <td>0.860546</td>\n",
       "      <td>0.023102</td>\n",
       "      <td>0.816332</td>\n",
       "      <td>0.845144</td>\n",
       "      <td>0.860816</td>\n",
       "      <td>0.876096</td>\n",
       "      <td>0.907035</td>\n",
       "      <td>0.001035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.7</th>\n",
       "      <td>0.868406</td>\n",
       "      <td>0.022665</td>\n",
       "      <td>0.824938</td>\n",
       "      <td>0.85218</td>\n",
       "      <td>0.868198</td>\n",
       "      <td>0.883496</td>\n",
       "      <td>0.916094</td>\n",
       "      <td>0.000975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.8</th>\n",
       "      <td>0.938247</td>\n",
       "      <td>0.024755</td>\n",
       "      <td>0.893371</td>\n",
       "      <td>0.920792</td>\n",
       "      <td>0.937449</td>\n",
       "      <td>0.95567</td>\n",
       "      <td>0.987215</td>\n",
       "      <td>0.000997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.9</th>\n",
       "      <td>1.502561</td>\n",
       "      <td>0.044321</td>\n",
       "      <td>1.419892</td>\n",
       "      <td>1.4733</td>\n",
       "      <td>1.502207</td>\n",
       "      <td>1.531066</td>\n",
       "      <td>1.590579</td>\n",
       "      <td>0.002008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>1.762392</td>\n",
       "      <td>0.043962</td>\n",
       "      <td>1.675322</td>\n",
       "      <td>1.738123</td>\n",
       "      <td>1.764136</td>\n",
       "      <td>1.789828</td>\n",
       "      <td>1.840772</td>\n",
       "      <td>0.001234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_std</th>\n",
       "      <td>0.127803</td>\n",
       "      <td>0.041082</td>\n",
       "      <td>0.077109</td>\n",
       "      <td>0.101991</td>\n",
       "      <td>0.119559</td>\n",
       "      <td>0.143427</td>\n",
       "      <td>0.229426</td>\n",
       "      <td>0.002211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.0</th>\n",
       "      <td>1.729844</td>\n",
       "      <td>0.001529</td>\n",
       "      <td>1.726595</td>\n",
       "      <td>1.728886</td>\n",
       "      <td>1.72993</td>\n",
       "      <td>1.730931</td>\n",
       "      <td>1.732523</td>\n",
       "      <td>0.000072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.1</th>\n",
       "      <td>1.87689</td>\n",
       "      <td>0.003689</td>\n",
       "      <td>1.869021</td>\n",
       "      <td>1.874347</td>\n",
       "      <td>1.877137</td>\n",
       "      <td>1.879502</td>\n",
       "      <td>1.883455</td>\n",
       "      <td>0.000176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.2</th>\n",
       "      <td>1.639075</td>\n",
       "      <td>0.002113</td>\n",
       "      <td>1.634718</td>\n",
       "      <td>1.637599</td>\n",
       "      <td>1.639228</td>\n",
       "      <td>1.640583</td>\n",
       "      <td>1.642865</td>\n",
       "      <td>0.000117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.3</th>\n",
       "      <td>1.739564</td>\n",
       "      <td>0.001898</td>\n",
       "      <td>1.735472</td>\n",
       "      <td>1.738401</td>\n",
       "      <td>1.739731</td>\n",
       "      <td>1.740943</td>\n",
       "      <td>1.742881</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.4</th>\n",
       "      <td>1.823038</td>\n",
       "      <td>0.001846</td>\n",
       "      <td>1.819245</td>\n",
       "      <td>1.821726</td>\n",
       "      <td>1.823124</td>\n",
       "      <td>1.824381</td>\n",
       "      <td>1.826401</td>\n",
       "      <td>0.000093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.5</th>\n",
       "      <td>1.780391</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>1.779954</td>\n",
       "      <td>1.780258</td>\n",
       "      <td>1.780402</td>\n",
       "      <td>1.780537</td>\n",
       "      <td>1.780764</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.6</th>\n",
       "      <td>1.77356</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>1.769764</td>\n",
       "      <td>1.77221</td>\n",
       "      <td>1.773659</td>\n",
       "      <td>1.774949</td>\n",
       "      <td>1.777243</td>\n",
       "      <td>0.000101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.7</th>\n",
       "      <td>1.59595</td>\n",
       "      <td>0.001856</td>\n",
       "      <td>1.592157</td>\n",
       "      <td>1.594688</td>\n",
       "      <td>1.596081</td>\n",
       "      <td>1.597251</td>\n",
       "      <td>1.59919</td>\n",
       "      <td>0.00009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.8</th>\n",
       "      <td>1.846581</td>\n",
       "      <td>0.002509</td>\n",
       "      <td>1.841257</td>\n",
       "      <td>1.845071</td>\n",
       "      <td>1.846809</td>\n",
       "      <td>1.848397</td>\n",
       "      <td>1.850974</td>\n",
       "      <td>0.000107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.9</th>\n",
       "      <td>1.955039</td>\n",
       "      <td>0.006197</td>\n",
       "      <td>1.941662</td>\n",
       "      <td>1.951385</td>\n",
       "      <td>1.955367</td>\n",
       "      <td>1.959284</td>\n",
       "      <td>1.966502</td>\n",
       "      <td>0.000334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.406423</td>\n",
       "      <td>0.01066</td>\n",
       "      <td>0.385663</td>\n",
       "      <td>0.39975</td>\n",
       "      <td>0.406726</td>\n",
       "      <td>0.41341</td>\n",
       "      <td>0.426428</td>\n",
       "      <td>0.000572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_std</th>\n",
       "      <td>0.111236</td>\n",
       "      <td>0.025033</td>\n",
       "      <td>0.065157</td>\n",
       "      <td>0.092933</td>\n",
       "      <td>0.110502</td>\n",
       "      <td>0.128712</td>\n",
       "      <td>0.162202</td>\n",
       "      <td>0.001416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.0</th>\n",
       "      <td>0.406625</td>\n",
       "      <td>0.014707</td>\n",
       "      <td>0.37849</td>\n",
       "      <td>0.396524</td>\n",
       "      <td>0.406768</td>\n",
       "      <td>0.416933</td>\n",
       "      <td>0.435086</td>\n",
       "      <td>0.000701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.1</th>\n",
       "      <td>0.424572</td>\n",
       "      <td>0.015686</td>\n",
       "      <td>0.394931</td>\n",
       "      <td>0.413721</td>\n",
       "      <td>0.424395</td>\n",
       "      <td>0.43493</td>\n",
       "      <td>0.456409</td>\n",
       "      <td>0.000735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.2</th>\n",
       "      <td>0.353131</td>\n",
       "      <td>0.017543</td>\n",
       "      <td>0.31857</td>\n",
       "      <td>0.341544</td>\n",
       "      <td>0.352933</td>\n",
       "      <td>0.365388</td>\n",
       "      <td>0.386623</td>\n",
       "      <td>0.001142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.3</th>\n",
       "      <td>0.378599</td>\n",
       "      <td>0.017765</td>\n",
       "      <td>0.34177</td>\n",
       "      <td>0.366784</td>\n",
       "      <td>0.378775</td>\n",
       "      <td>0.391128</td>\n",
       "      <td>0.411987</td>\n",
       "      <td>0.001032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.4</th>\n",
       "      <td>0.382394</td>\n",
       "      <td>0.015922</td>\n",
       "      <td>0.351524</td>\n",
       "      <td>0.37191</td>\n",
       "      <td>0.382518</td>\n",
       "      <td>0.392776</td>\n",
       "      <td>0.415149</td>\n",
       "      <td>0.000902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.5</th>\n",
       "      <td>0.447408</td>\n",
       "      <td>0.014668</td>\n",
       "      <td>0.420445</td>\n",
       "      <td>0.436486</td>\n",
       "      <td>0.447683</td>\n",
       "      <td>0.456966</td>\n",
       "      <td>0.477137</td>\n",
       "      <td>0.000679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.6</th>\n",
       "      <td>0.42044</td>\n",
       "      <td>0.015184</td>\n",
       "      <td>0.391129</td>\n",
       "      <td>0.409982</td>\n",
       "      <td>0.42031</td>\n",
       "      <td>0.430641</td>\n",
       "      <td>0.450093</td>\n",
       "      <td>0.000757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.7</th>\n",
       "      <td>0.409145</td>\n",
       "      <td>0.014639</td>\n",
       "      <td>0.380417</td>\n",
       "      <td>0.399482</td>\n",
       "      <td>0.408315</td>\n",
       "      <td>0.419389</td>\n",
       "      <td>0.437577</td>\n",
       "      <td>0.000753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.8</th>\n",
       "      <td>0.429067</td>\n",
       "      <td>0.014567</td>\n",
       "      <td>0.401969</td>\n",
       "      <td>0.419554</td>\n",
       "      <td>0.428613</td>\n",
       "      <td>0.438753</td>\n",
       "      <td>0.456761</td>\n",
       "      <td>0.000712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.9</th>\n",
       "      <td>0.414252</td>\n",
       "      <td>0.016411</td>\n",
       "      <td>0.381777</td>\n",
       "      <td>0.40343</td>\n",
       "      <td>0.414459</td>\n",
       "      <td>0.425008</td>\n",
       "      <td>0.448023</td>\n",
       "      <td>0.000921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept</th>\n",
       "      <td>-2.564308</td>\n",
       "      <td>0.126282</td>\n",
       "      <td>-2.822149</td>\n",
       "      <td>-2.649338</td>\n",
       "      <td>-2.558347</td>\n",
       "      <td>-2.478945</td>\n",
       "      <td>-2.32167</td>\n",
       "      <td>0.007415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_std</th>\n",
       "      <td>0.266703</td>\n",
       "      <td>0.139694</td>\n",
       "      <td>0.06281</td>\n",
       "      <td>0.174939</td>\n",
       "      <td>0.249346</td>\n",
       "      <td>0.33407</td>\n",
       "      <td>0.583887</td>\n",
       "      <td>0.009969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj.0</th>\n",
       "      <td>-2.564306</td>\n",
       "      <td>0.180271</td>\n",
       "      <td>-2.932611</td>\n",
       "      <td>-2.685864</td>\n",
       "      <td>-2.549999</td>\n",
       "      <td>-2.445731</td>\n",
       "      <td>-2.21466</td>\n",
       "      <td>0.009261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj.1</th>\n",
       "      <td>-2.5317</td>\n",
       "      <td>0.147554</td>\n",
       "      <td>-2.824173</td>\n",
       "      <td>-2.62519</td>\n",
       "      <td>-2.532631</td>\n",
       "      <td>-2.436262</td>\n",
       "      <td>-2.225892</td>\n",
       "      <td>0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj.2</th>\n",
       "      <td>-2.668607</td>\n",
       "      <td>0.180918</td>\n",
       "      <td>-3.030569</td>\n",
       "      <td>-2.791183</td>\n",
       "      <td>-2.659031</td>\n",
       "      <td>-2.540405</td>\n",
       "      <td>-2.33725</td>\n",
       "      <td>0.01203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj.3</th>\n",
       "      <td>-2.635311</td>\n",
       "      <td>0.170992</td>\n",
       "      <td>-2.978902</td>\n",
       "      <td>-2.748357</td>\n",
       "      <td>-2.624429</td>\n",
       "      <td>-2.515199</td>\n",
       "      <td>-2.307669</td>\n",
       "      <td>0.009669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj.4</th>\n",
       "      <td>-2.708448</td>\n",
       "      <td>0.183442</td>\n",
       "      <td>-3.072997</td>\n",
       "      <td>-2.845649</td>\n",
       "      <td>-2.695472</td>\n",
       "      <td>-2.565951</td>\n",
       "      <td>-2.382747</td>\n",
       "      <td>0.011774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj.5</th>\n",
       "      <td>-2.405105</td>\n",
       "      <td>0.280304</td>\n",
       "      <td>-2.907138</td>\n",
       "      <td>-2.568382</td>\n",
       "      <td>-2.432111</td>\n",
       "      <td>-2.259304</td>\n",
       "      <td>-1.722973</td>\n",
       "      <td>0.013475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj.6</th>\n",
       "      <td>-2.769208</td>\n",
       "      <td>0.206948</td>\n",
       "      <td>-3.249535</td>\n",
       "      <td>-2.90634</td>\n",
       "      <td>-2.750296</td>\n",
       "      <td>-2.614345</td>\n",
       "      <td>-2.426095</td>\n",
       "      <td>0.014733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj.7</th>\n",
       "      <td>-2.767714</td>\n",
       "      <td>0.219967</td>\n",
       "      <td>-3.267377</td>\n",
       "      <td>-2.908245</td>\n",
       "      <td>-2.75322</td>\n",
       "      <td>-2.59615</td>\n",
       "      <td>-2.411447</td>\n",
       "      <td>0.016548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj.8</th>\n",
       "      <td>-2.404853</td>\n",
       "      <td>0.152175</td>\n",
       "      <td>-2.68945</td>\n",
       "      <td>-2.503733</td>\n",
       "      <td>-2.422783</td>\n",
       "      <td>-2.309908</td>\n",
       "      <td>-2.078345</td>\n",
       "      <td>0.008374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj.9</th>\n",
       "      <td>-2.300825</td>\n",
       "      <td>0.143489</td>\n",
       "      <td>-2.558598</td>\n",
       "      <td>-2.402456</td>\n",
       "      <td>-2.30793</td>\n",
       "      <td>-2.205768</td>\n",
       "      <td>-1.997823</td>\n",
       "      <td>0.010462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg</th>\n",
       "      <td>2.641619</td>\n",
       "      <td>0.163979</td>\n",
       "      <td>2.344999</td>\n",
       "      <td>2.542586</td>\n",
       "      <td>2.640943</td>\n",
       "      <td>2.733638</td>\n",
       "      <td>2.971078</td>\n",
       "      <td>0.00799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_std</th>\n",
       "      <td>0.403579</td>\n",
       "      <td>0.174272</td>\n",
       "      <td>0.168623</td>\n",
       "      <td>0.287279</td>\n",
       "      <td>0.368513</td>\n",
       "      <td>0.488823</td>\n",
       "      <td>0.848182</td>\n",
       "      <td>0.012065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_subj.0</th>\n",
       "      <td>2.590544</td>\n",
       "      <td>0.226646</td>\n",
       "      <td>2.175058</td>\n",
       "      <td>2.442574</td>\n",
       "      <td>2.582727</td>\n",
       "      <td>2.736542</td>\n",
       "      <td>3.022326</td>\n",
       "      <td>0.010809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_subj.1</th>\n",
       "      <td>2.888344</td>\n",
       "      <td>0.174058</td>\n",
       "      <td>2.55296</td>\n",
       "      <td>2.769682</td>\n",
       "      <td>2.882718</td>\n",
       "      <td>3.00708</td>\n",
       "      <td>3.221037</td>\n",
       "      <td>0.009499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_subj.2</th>\n",
       "      <td>2.693351</td>\n",
       "      <td>0.20563</td>\n",
       "      <td>2.3095</td>\n",
       "      <td>2.54631</td>\n",
       "      <td>2.696276</td>\n",
       "      <td>2.826709</td>\n",
       "      <td>3.103248</td>\n",
       "      <td>0.011171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_subj.3</th>\n",
       "      <td>2.373187</td>\n",
       "      <td>0.215628</td>\n",
       "      <td>1.98237</td>\n",
       "      <td>2.225528</td>\n",
       "      <td>2.374955</td>\n",
       "      <td>2.51508</td>\n",
       "      <td>2.795833</td>\n",
       "      <td>0.010147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_subj.4</th>\n",
       "      <td>2.710849</td>\n",
       "      <td>0.207885</td>\n",
       "      <td>2.332881</td>\n",
       "      <td>2.569179</td>\n",
       "      <td>2.695788</td>\n",
       "      <td>2.849818</td>\n",
       "      <td>3.112974</td>\n",
       "      <td>0.011238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_subj.5</th>\n",
       "      <td>2.974687</td>\n",
       "      <td>0.391147</td>\n",
       "      <td>2.367593</td>\n",
       "      <td>2.706481</td>\n",
       "      <td>2.91585</td>\n",
       "      <td>3.189896</td>\n",
       "      <td>3.881042</td>\n",
       "      <td>0.018991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_subj.6</th>\n",
       "      <td>2.919721</td>\n",
       "      <td>0.233801</td>\n",
       "      <td>2.493308</td>\n",
       "      <td>2.754915</td>\n",
       "      <td>2.911823</td>\n",
       "      <td>3.064192</td>\n",
       "      <td>3.430876</td>\n",
       "      <td>0.014685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_subj.7</th>\n",
       "      <td>2.807175</td>\n",
       "      <td>0.240712</td>\n",
       "      <td>2.36721</td>\n",
       "      <td>2.637367</td>\n",
       "      <td>2.797168</td>\n",
       "      <td>2.963463</td>\n",
       "      <td>3.331768</td>\n",
       "      <td>0.016782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_subj.8</th>\n",
       "      <td>2.431325</td>\n",
       "      <td>0.188242</td>\n",
       "      <td>2.032507</td>\n",
       "      <td>2.306844</td>\n",
       "      <td>2.44516</td>\n",
       "      <td>2.558341</td>\n",
       "      <td>2.786851</td>\n",
       "      <td>0.008901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_my_condition_reg_subj.9</th>\n",
       "      <td>2.10165</td>\n",
       "      <td>0.165925</td>\n",
       "      <td>1.772621</td>\n",
       "      <td>1.990586</td>\n",
       "      <td>2.1084</td>\n",
       "      <td>2.219474</td>\n",
       "      <td>2.409722</td>\n",
       "      <td>0.011511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               mean       std      2.5q       25q       50q  \\\n",
       "a                          0.979899  0.157335  0.748584  0.876946  0.959024   \n",
       "a_std                       0.46193  0.170778  0.253589  0.350913  0.423853   \n",
       "a_subj.0                   0.754792  0.019591  0.719274   0.74053  0.754511   \n",
       "a_subj.1                   1.186801  0.032068  1.127424   1.16474  1.185715   \n",
       "a_subj.2                   0.984794  0.027371  0.930539  0.966547  0.984786   \n",
       "a_subj.3                   0.852952  0.023745  0.807877  0.835383  0.854175   \n",
       "a_subj.4                   0.852153  0.022061  0.811592  0.836282   0.85154   \n",
       "a_subj.5                   0.255451  0.006012  0.244435  0.251348  0.255234   \n",
       "a_subj.6                   0.860546  0.023102  0.816332  0.845144  0.860816   \n",
       "a_subj.7                   0.868406  0.022665  0.824938   0.85218  0.868198   \n",
       "a_subj.8                   0.938247  0.024755  0.893371  0.920792  0.937449   \n",
       "a_subj.9                   1.502561  0.044321  1.419892    1.4733  1.502207   \n",
       "t                          1.762392  0.043962  1.675322  1.738123  1.764136   \n",
       "t_std                      0.127803  0.041082  0.077109  0.101991  0.119559   \n",
       "t_subj.0                   1.729844  0.001529  1.726595  1.728886   1.72993   \n",
       "t_subj.1                    1.87689  0.003689  1.869021  1.874347  1.877137   \n",
       "t_subj.2                   1.639075  0.002113  1.634718  1.637599  1.639228   \n",
       "t_subj.3                   1.739564  0.001898  1.735472  1.738401  1.739731   \n",
       "t_subj.4                   1.823038  0.001846  1.819245  1.821726  1.823124   \n",
       "t_subj.5                   1.780391  0.000207  1.779954  1.780258  1.780402   \n",
       "t_subj.6                    1.77356  0.001995  1.769764   1.77221  1.773659   \n",
       "t_subj.7                    1.59595  0.001856  1.592157  1.594688  1.596081   \n",
       "t_subj.8                   1.846581  0.002509  1.841257  1.845071  1.846809   \n",
       "t_subj.9                   1.955039  0.006197  1.941662  1.951385  1.955367   \n",
       "z                          0.406423   0.01066  0.385663   0.39975  0.406726   \n",
       "z_std                      0.111236  0.025033  0.065157  0.092933  0.110502   \n",
       "z_subj.0                   0.406625  0.014707   0.37849  0.396524  0.406768   \n",
       "z_subj.1                   0.424572  0.015686  0.394931  0.413721  0.424395   \n",
       "z_subj.2                   0.353131  0.017543   0.31857  0.341544  0.352933   \n",
       "z_subj.3                   0.378599  0.017765   0.34177  0.366784  0.378775   \n",
       "z_subj.4                   0.382394  0.015922  0.351524   0.37191  0.382518   \n",
       "z_subj.5                   0.447408  0.014668  0.420445  0.436486  0.447683   \n",
       "z_subj.6                    0.42044  0.015184  0.391129  0.409982   0.42031   \n",
       "z_subj.7                   0.409145  0.014639  0.380417  0.399482  0.408315   \n",
       "z_subj.8                   0.429067  0.014567  0.401969  0.419554  0.428613   \n",
       "z_subj.9                   0.414252  0.016411  0.381777   0.40343  0.414459   \n",
       "v_Intercept               -2.564308  0.126282 -2.822149 -2.649338 -2.558347   \n",
       "v_Intercept_std            0.266703  0.139694   0.06281  0.174939  0.249346   \n",
       "v_Intercept_subj.0        -2.564306  0.180271 -2.932611 -2.685864 -2.549999   \n",
       "v_Intercept_subj.1          -2.5317  0.147554 -2.824173  -2.62519 -2.532631   \n",
       "v_Intercept_subj.2        -2.668607  0.180918 -3.030569 -2.791183 -2.659031   \n",
       "v_Intercept_subj.3        -2.635311  0.170992 -2.978902 -2.748357 -2.624429   \n",
       "v_Intercept_subj.4        -2.708448  0.183442 -3.072997 -2.845649 -2.695472   \n",
       "v_Intercept_subj.5        -2.405105  0.280304 -2.907138 -2.568382 -2.432111   \n",
       "v_Intercept_subj.6        -2.769208  0.206948 -3.249535  -2.90634 -2.750296   \n",
       "v_Intercept_subj.7        -2.767714  0.219967 -3.267377 -2.908245  -2.75322   \n",
       "v_Intercept_subj.8        -2.404853  0.152175  -2.68945 -2.503733 -2.422783   \n",
       "v_Intercept_subj.9        -2.300825  0.143489 -2.558598 -2.402456  -2.30793   \n",
       "v_my_condition_reg         2.641619  0.163979  2.344999  2.542586  2.640943   \n",
       "v_my_condition_reg_std     0.403579  0.174272  0.168623  0.287279  0.368513   \n",
       "v_my_condition_reg_subj.0  2.590544  0.226646  2.175058  2.442574  2.582727   \n",
       "v_my_condition_reg_subj.1  2.888344  0.174058   2.55296  2.769682  2.882718   \n",
       "v_my_condition_reg_subj.2  2.693351   0.20563    2.3095   2.54631  2.696276   \n",
       "v_my_condition_reg_subj.3  2.373187  0.215628   1.98237  2.225528  2.374955   \n",
       "v_my_condition_reg_subj.4  2.710849  0.207885  2.332881  2.569179  2.695788   \n",
       "v_my_condition_reg_subj.5  2.974687  0.391147  2.367593  2.706481   2.91585   \n",
       "v_my_condition_reg_subj.6  2.919721  0.233801  2.493308  2.754915  2.911823   \n",
       "v_my_condition_reg_subj.7  2.807175  0.240712   2.36721  2.637367  2.797168   \n",
       "v_my_condition_reg_subj.8  2.431325  0.188242  2.032507  2.306844   2.44516   \n",
       "v_my_condition_reg_subj.9   2.10165  0.165925  1.772621  1.990586    2.1084   \n",
       "\n",
       "                                75q     97.5q    mc err  \n",
       "a                          1.054162  1.311815  0.008575  \n",
       "a_std                      0.527927  0.930596  0.010485  \n",
       "a_subj.0                   0.767652  0.797013  0.000781  \n",
       "a_subj.1                    1.20769  1.252286  0.001459  \n",
       "a_subj.2                   1.004296  1.039688  0.001087  \n",
       "a_subj.3                   0.870471  0.896436  0.001074  \n",
       "a_subj.4                   0.867075  0.895638    0.0009  \n",
       "a_subj.5                    0.25918  0.268033  0.000256  \n",
       "a_subj.6                   0.876096  0.907035  0.001035  \n",
       "a_subj.7                   0.883496  0.916094  0.000975  \n",
       "a_subj.8                    0.95567  0.987215  0.000997  \n",
       "a_subj.9                   1.531066  1.590579  0.002008  \n",
       "t                          1.789828  1.840772  0.001234  \n",
       "t_std                      0.143427  0.229426  0.002211  \n",
       "t_subj.0                   1.730931  1.732523  0.000072  \n",
       "t_subj.1                   1.879502  1.883455  0.000176  \n",
       "t_subj.2                   1.640583  1.642865  0.000117  \n",
       "t_subj.3                   1.740943  1.742881  0.000109  \n",
       "t_subj.4                   1.824381  1.826401  0.000093  \n",
       "t_subj.5                   1.780537  1.780764   0.00001  \n",
       "t_subj.6                   1.774949  1.777243  0.000101  \n",
       "t_subj.7                   1.597251   1.59919   0.00009  \n",
       "t_subj.8                   1.848397  1.850974  0.000107  \n",
       "t_subj.9                   1.959284  1.966502  0.000334  \n",
       "z                           0.41341  0.426428  0.000572  \n",
       "z_std                      0.128712  0.162202  0.001416  \n",
       "z_subj.0                   0.416933  0.435086  0.000701  \n",
       "z_subj.1                    0.43493  0.456409  0.000735  \n",
       "z_subj.2                   0.365388  0.386623  0.001142  \n",
       "z_subj.3                   0.391128  0.411987  0.001032  \n",
       "z_subj.4                   0.392776  0.415149  0.000902  \n",
       "z_subj.5                   0.456966  0.477137  0.000679  \n",
       "z_subj.6                   0.430641  0.450093  0.000757  \n",
       "z_subj.7                   0.419389  0.437577  0.000753  \n",
       "z_subj.8                   0.438753  0.456761  0.000712  \n",
       "z_subj.9                   0.425008  0.448023  0.000921  \n",
       "v_Intercept               -2.478945  -2.32167  0.007415  \n",
       "v_Intercept_std             0.33407  0.583887  0.009969  \n",
       "v_Intercept_subj.0        -2.445731  -2.21466  0.009261  \n",
       "v_Intercept_subj.1        -2.436262 -2.225892  0.008715  \n",
       "v_Intercept_subj.2        -2.540405  -2.33725   0.01203  \n",
       "v_Intercept_subj.3        -2.515199 -2.307669  0.009669  \n",
       "v_Intercept_subj.4        -2.565951 -2.382747  0.011774  \n",
       "v_Intercept_subj.5        -2.259304 -1.722973  0.013475  \n",
       "v_Intercept_subj.6        -2.614345 -2.426095  0.014733  \n",
       "v_Intercept_subj.7         -2.59615 -2.411447  0.016548  \n",
       "v_Intercept_subj.8        -2.309908 -2.078345  0.008374  \n",
       "v_Intercept_subj.9        -2.205768 -1.997823  0.010462  \n",
       "v_my_condition_reg         2.733638  2.971078   0.00799  \n",
       "v_my_condition_reg_std     0.488823  0.848182  0.012065  \n",
       "v_my_condition_reg_subj.0  2.736542  3.022326  0.010809  \n",
       "v_my_condition_reg_subj.1   3.00708  3.221037  0.009499  \n",
       "v_my_condition_reg_subj.2  2.826709  3.103248  0.011171  \n",
       "v_my_condition_reg_subj.3   2.51508  2.795833  0.010147  \n",
       "v_my_condition_reg_subj.4  2.849818  3.112974  0.011238  \n",
       "v_my_condition_reg_subj.5  3.189896  3.881042  0.018991  \n",
       "v_my_condition_reg_subj.6  3.064192  3.430876  0.014685  \n",
       "v_my_condition_reg_subj.7  2.963463  3.331768  0.016782  \n",
       "v_my_condition_reg_subj.8  2.558341  2.786851  0.008901  \n",
       "v_my_condition_reg_subj.9  2.219474  2.409722  0.011511  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_group_reg[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c21c887a-082f-4a03-8e2f-43d6d24c820f",
   "metadata": {},
   "source": [
    "#### Regression + *depends_on*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d66e67b1-7a50-412f-b00b-1561cf490cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate some data\n",
    "model = \"ddm_hddm_base\"\n",
    "n_samples = 200\n",
    "\n",
    "data, parameter_dict = simulator_h_c(\n",
    "    n_subjects=10,\n",
    "    n_trials_per_subject=n_samples,\n",
    "    model=model,\n",
    "    p_outlier=0.00,\n",
    "    conditions={\"my_condition\": [\"high\", \"low\"]},\n",
    "    depends_on={\"v\": [\"my_condition\"]},\n",
    "    regression_models=None,\n",
    "    regression_covariates=None,\n",
    "    group_only_regressors=False,\n",
    "    group_only=None,\n",
    "    fixed_at_default=None,\n",
    ")\n",
    "\n",
    "data_new = deepcopy(data)\n",
    "data_new[\"my_condition_reg\"] = 0\n",
    "data_new.loc[data[\"my_condition\"] == \"high\", \"my_condition_reg\"] = 1\n",
    "data_new[\"randcol\"] = np.random.uniform(low=-1, high=1, size=data_new.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5d0c8e3c-0b04-4f98-b05b-40f4e7dbe2a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'z': 0.5908694905479872,\n",
       " 'z_std': 0.05841432353345733,\n",
       " 'a': 2.8462292424112334,\n",
       " 'a_std': 0.4737361644173384,\n",
       " 't': 1.636870513835886,\n",
       " 't_std': 0.016405750830200613,\n",
       " 'v(high)': -1.8846064812128618,\n",
       " 'v(low)': 2.134713435020661,\n",
       " 'v_std': 0.08762170340692278,\n",
       " 'z_subj.0': 0.5064428114134574,\n",
       " 'a_subj.0': 3.0709551509801605,\n",
       " 't_subj.0': 1.6415888992423575,\n",
       " 'v_subj(high).0': -1.7865297359499341,\n",
       " 'z_subj.1': 0.4649051477142965,\n",
       " 'a_subj.1': 3.3983638177278914,\n",
       " 't_subj.1': 1.6408223809114115,\n",
       " 'v_subj(high).1': -1.995621356778327,\n",
       " 'z_subj.2': 0.5252462562085992,\n",
       " 'a_subj.2': 3.1056193248757116,\n",
       " 't_subj.2': 1.646923228148418,\n",
       " 'v_subj(high).2': -1.832008254679154,\n",
       " 'z_subj.3': 0.5493734288630902,\n",
       " 'a_subj.3': 3.1486837753440367,\n",
       " 't_subj.3': 1.6298920039096065,\n",
       " 'v_subj(high).3': -1.905966366325631,\n",
       " 'z_subj.4': 0.5366140696909004,\n",
       " 'a_subj.4': 2.679292457679929,\n",
       " 't_subj.4': 1.6393145184868347,\n",
       " 'v_subj(high).4': -1.8359282983554415,\n",
       " 'z_subj.5': 0.5601118003081349,\n",
       " 'a_subj.5': 2.515191881370122,\n",
       " 't_subj.5': 1.6271327352437392,\n",
       " 'v_subj(high).5': -1.7935818929431258,\n",
       " 'z_subj.6': 0.5134765510882167,\n",
       " 'a_subj.6': 3.3593997534870295,\n",
       " 't_subj.6': 1.629104765857765,\n",
       " 'v_subj(high).6': -1.9859192365041223,\n",
       " 'z_subj.7': 0.4694508149866473,\n",
       " 'a_subj.7': 2.9824958423810393,\n",
       " 't_subj.7': 1.6384749426760061,\n",
       " 'v_subj(high).7': -1.86859543717404,\n",
       " 'z_subj.8': 0.49934430337883096,\n",
       " 'a_subj.8': 2.997901628521727,\n",
       " 't_subj.8': 1.640434643075955,\n",
       " 'v_subj(high).8': -1.94296944831911,\n",
       " 'z_subj.9': 0.6631225785474723,\n",
       " 'a_subj.9': 2.4638222626941886,\n",
       " 't_subj.9': 1.6086933179067378,\n",
       " 'v_subj(high).9': -1.8672346376677027,\n",
       " 'v_subj(low).0': 2.0712440998924753,\n",
       " 'v_subj(low).1': 2.152048859552784,\n",
       " 'v_subj(low).2': 2.179561645533514,\n",
       " 'v_subj(low).3': 2.2340209413837204,\n",
       " 'v_subj(low).4': 2.0573379319086618,\n",
       " 'v_subj(low).5': 2.2834061170729867,\n",
       " 'v_subj(low).6': 2.1317614453326166,\n",
       " 'v_subj(low).7': 1.9714550304636318,\n",
       " 'v_subj(low).8': 2.1217063448138505,\n",
       " 'v_subj(low).9': 1.996355851159009}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameter_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "16fbf383-32b8-4531-9164-08b15065ec72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 1016.8 sec1\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 782.2 sec"
     ]
    }
   ],
   "source": [
    "hddm_models_group_reg_depends = []\n",
    "\n",
    "\n",
    "def identity_link(x):\n",
    "    return x\n",
    "\n",
    "\n",
    "reg_models = [{\"model\": \"v ~ 1 + randcol\", \"link_func\": identity_link}]\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_group_reg_depends.append(\n",
    "        hddm.HDDMRegressor(\n",
    "            data_new,\n",
    "            reg_models,\n",
    "            depends_on={\"v\": [\"my_condition\"]},\n",
    "            informative=True,\n",
    "            is_group_model=True,\n",
    "            std_depends=True,\n",
    "            group_only_regressors=False,\n",
    "            include=[\"v\", \"a\", \"z\", \"t\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 2000\n",
    "for i in range(n_models):\n",
    "    print(i)\n",
    "    hddm_models_group_reg_depends[i].sample(nmcmc, burn=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2f09e300-3f69-4da8-a065-f381f13366fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>z_std</th>\n",
       "      <td>0.149831</td>\n",
       "      <td>0.02301</td>\n",
       "      <td>0.108674</td>\n",
       "      <td>0.133286</td>\n",
       "      <td>0.148719</td>\n",
       "      <td>0.165521</td>\n",
       "      <td>0.198529</td>\n",
       "      <td>0.000997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.0</th>\n",
       "      <td>0.515349</td>\n",
       "      <td>0.01346</td>\n",
       "      <td>0.489155</td>\n",
       "      <td>0.506382</td>\n",
       "      <td>0.515394</td>\n",
       "      <td>0.524261</td>\n",
       "      <td>0.540636</td>\n",
       "      <td>0.000602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.1</th>\n",
       "      <td>0.465093</td>\n",
       "      <td>0.014649</td>\n",
       "      <td>0.438685</td>\n",
       "      <td>0.454764</td>\n",
       "      <td>0.464928</td>\n",
       "      <td>0.475174</td>\n",
       "      <td>0.493847</td>\n",
       "      <td>0.000788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.2</th>\n",
       "      <td>0.526775</td>\n",
       "      <td>0.013571</td>\n",
       "      <td>0.497615</td>\n",
       "      <td>0.517695</td>\n",
       "      <td>0.527443</td>\n",
       "      <td>0.536549</td>\n",
       "      <td>0.551951</td>\n",
       "      <td>0.000726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.3</th>\n",
       "      <td>0.545035</td>\n",
       "      <td>0.013769</td>\n",
       "      <td>0.519178</td>\n",
       "      <td>0.536016</td>\n",
       "      <td>0.544688</td>\n",
       "      <td>0.554478</td>\n",
       "      <td>0.5732</td>\n",
       "      <td>0.000691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.4</th>\n",
       "      <td>0.526629</td>\n",
       "      <td>0.013767</td>\n",
       "      <td>0.49775</td>\n",
       "      <td>0.517467</td>\n",
       "      <td>0.526199</td>\n",
       "      <td>0.536063</td>\n",
       "      <td>0.554221</td>\n",
       "      <td>0.000643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.5</th>\n",
       "      <td>0.557127</td>\n",
       "      <td>0.014653</td>\n",
       "      <td>0.524782</td>\n",
       "      <td>0.547921</td>\n",
       "      <td>0.55711</td>\n",
       "      <td>0.566938</td>\n",
       "      <td>0.58437</td>\n",
       "      <td>0.000808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.6</th>\n",
       "      <td>0.503098</td>\n",
       "      <td>0.013541</td>\n",
       "      <td>0.477167</td>\n",
       "      <td>0.494025</td>\n",
       "      <td>0.503112</td>\n",
       "      <td>0.51188</td>\n",
       "      <td>0.532034</td>\n",
       "      <td>0.000713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.7</th>\n",
       "      <td>0.473114</td>\n",
       "      <td>0.014068</td>\n",
       "      <td>0.446153</td>\n",
       "      <td>0.463843</td>\n",
       "      <td>0.472419</td>\n",
       "      <td>0.483024</td>\n",
       "      <td>0.501168</td>\n",
       "      <td>0.00073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.8</th>\n",
       "      <td>0.485062</td>\n",
       "      <td>0.014241</td>\n",
       "      <td>0.456993</td>\n",
       "      <td>0.475948</td>\n",
       "      <td>0.485222</td>\n",
       "      <td>0.494706</td>\n",
       "      <td>0.513391</td>\n",
       "      <td>0.000777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.9</th>\n",
       "      <td>0.631069</td>\n",
       "      <td>0.015014</td>\n",
       "      <td>0.600845</td>\n",
       "      <td>0.620559</td>\n",
       "      <td>0.631407</td>\n",
       "      <td>0.64011</td>\n",
       "      <td>0.661067</td>\n",
       "      <td>0.000929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept(high)</th>\n",
       "      <td>-1.854035</td>\n",
       "      <td>0.057133</td>\n",
       "      <td>-1.968593</td>\n",
       "      <td>-1.890305</td>\n",
       "      <td>-1.855993</td>\n",
       "      <td>-1.816348</td>\n",
       "      <td>-1.740457</td>\n",
       "      <td>0.003367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept(low)</th>\n",
       "      <td>2.143171</td>\n",
       "      <td>0.061703</td>\n",
       "      <td>2.018767</td>\n",
       "      <td>2.103625</td>\n",
       "      <td>2.144225</td>\n",
       "      <td>2.185435</td>\n",
       "      <td>2.263962</td>\n",
       "      <td>0.004015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_std(high)</th>\n",
       "      <td>0.117751</td>\n",
       "      <td>0.062801</td>\n",
       "      <td>0.0246</td>\n",
       "      <td>0.072532</td>\n",
       "      <td>0.109722</td>\n",
       "      <td>0.150339</td>\n",
       "      <td>0.264118</td>\n",
       "      <td>0.004854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_std(low)</th>\n",
       "      <td>0.108641</td>\n",
       "      <td>0.066725</td>\n",
       "      <td>0.008619</td>\n",
       "      <td>0.061176</td>\n",
       "      <td>0.101519</td>\n",
       "      <td>0.142855</td>\n",
       "      <td>0.281003</td>\n",
       "      <td>0.005491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj(high).0</th>\n",
       "      <td>-1.887503</td>\n",
       "      <td>0.091354</td>\n",
       "      <td>-2.082274</td>\n",
       "      <td>-1.943069</td>\n",
       "      <td>-1.881056</td>\n",
       "      <td>-1.83045</td>\n",
       "      <td>-1.708685</td>\n",
       "      <td>0.005045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj(high).1</th>\n",
       "      <td>-1.838828</td>\n",
       "      <td>0.080672</td>\n",
       "      <td>-1.997503</td>\n",
       "      <td>-1.887257</td>\n",
       "      <td>-1.83906</td>\n",
       "      <td>-1.788356</td>\n",
       "      <td>-1.676289</td>\n",
       "      <td>0.004631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj(high).2</th>\n",
       "      <td>-1.829501</td>\n",
       "      <td>0.081379</td>\n",
       "      <td>-1.992735</td>\n",
       "      <td>-1.879889</td>\n",
       "      <td>-1.828272</td>\n",
       "      <td>-1.783263</td>\n",
       "      <td>-1.661354</td>\n",
       "      <td>0.004157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj(high).3</th>\n",
       "      <td>-1.867723</td>\n",
       "      <td>0.081186</td>\n",
       "      <td>-2.026232</td>\n",
       "      <td>-1.922039</td>\n",
       "      <td>-1.865995</td>\n",
       "      <td>-1.814767</td>\n",
       "      <td>-1.694395</td>\n",
       "      <td>0.004373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj(high).4</th>\n",
       "      <td>-1.789013</td>\n",
       "      <td>0.08556</td>\n",
       "      <td>-1.947301</td>\n",
       "      <td>-1.846248</td>\n",
       "      <td>-1.790043</td>\n",
       "      <td>-1.736074</td>\n",
       "      <td>-1.610692</td>\n",
       "      <td>0.004896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj(high).5</th>\n",
       "      <td>-1.774274</td>\n",
       "      <td>0.089877</td>\n",
       "      <td>-1.942085</td>\n",
       "      <td>-1.833134</td>\n",
       "      <td>-1.783586</td>\n",
       "      <td>-1.721493</td>\n",
       "      <td>-1.580278</td>\n",
       "      <td>0.005394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj(high).6</th>\n",
       "      <td>-1.90966</td>\n",
       "      <td>0.089956</td>\n",
       "      <td>-2.095602</td>\n",
       "      <td>-1.968438</td>\n",
       "      <td>-1.903093</td>\n",
       "      <td>-1.844415</td>\n",
       "      <td>-1.750168</td>\n",
       "      <td>0.005198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj(high).7</th>\n",
       "      <td>-1.932482</td>\n",
       "      <td>0.104444</td>\n",
       "      <td>-2.159911</td>\n",
       "      <td>-1.997803</td>\n",
       "      <td>-1.923021</td>\n",
       "      <td>-1.853874</td>\n",
       "      <td>-1.758545</td>\n",
       "      <td>0.007063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj(high).8</th>\n",
       "      <td>-1.93726</td>\n",
       "      <td>0.103167</td>\n",
       "      <td>-2.17164</td>\n",
       "      <td>-1.996201</td>\n",
       "      <td>-1.926672</td>\n",
       "      <td>-1.864916</td>\n",
       "      <td>-1.762385</td>\n",
       "      <td>0.007038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept_subj(high).9</th>\n",
       "      <td>-1.765546</td>\n",
       "      <td>0.092618</td>\n",
       "      <td>-1.932878</td>\n",
       "      <td>-1.82642</td>\n",
       "      <td>-1.774931</td>\n",
       "      <td>-1.711732</td>\n",
       "      <td>-1.553556</td>\n",
       "      <td>0.005624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mean       std      2.5q       25q       50q  \\\n",
       "z_std                     0.149831   0.02301  0.108674  0.133286  0.148719   \n",
       "z_subj.0                  0.515349   0.01346  0.489155  0.506382  0.515394   \n",
       "z_subj.1                  0.465093  0.014649  0.438685  0.454764  0.464928   \n",
       "z_subj.2                  0.526775  0.013571  0.497615  0.517695  0.527443   \n",
       "z_subj.3                  0.545035  0.013769  0.519178  0.536016  0.544688   \n",
       "z_subj.4                  0.526629  0.013767   0.49775  0.517467  0.526199   \n",
       "z_subj.5                  0.557127  0.014653  0.524782  0.547921   0.55711   \n",
       "z_subj.6                  0.503098  0.013541  0.477167  0.494025  0.503112   \n",
       "z_subj.7                  0.473114  0.014068  0.446153  0.463843  0.472419   \n",
       "z_subj.8                  0.485062  0.014241  0.456993  0.475948  0.485222   \n",
       "z_subj.9                  0.631069  0.015014  0.600845  0.620559  0.631407   \n",
       "v_Intercept(high)        -1.854035  0.057133 -1.968593 -1.890305 -1.855993   \n",
       "v_Intercept(low)          2.143171  0.061703  2.018767  2.103625  2.144225   \n",
       "v_Intercept_std(high)     0.117751  0.062801    0.0246  0.072532  0.109722   \n",
       "v_Intercept_std(low)      0.108641  0.066725  0.008619  0.061176  0.101519   \n",
       "v_Intercept_subj(high).0 -1.887503  0.091354 -2.082274 -1.943069 -1.881056   \n",
       "v_Intercept_subj(high).1 -1.838828  0.080672 -1.997503 -1.887257  -1.83906   \n",
       "v_Intercept_subj(high).2 -1.829501  0.081379 -1.992735 -1.879889 -1.828272   \n",
       "v_Intercept_subj(high).3 -1.867723  0.081186 -2.026232 -1.922039 -1.865995   \n",
       "v_Intercept_subj(high).4 -1.789013   0.08556 -1.947301 -1.846248 -1.790043   \n",
       "v_Intercept_subj(high).5 -1.774274  0.089877 -1.942085 -1.833134 -1.783586   \n",
       "v_Intercept_subj(high).6  -1.90966  0.089956 -2.095602 -1.968438 -1.903093   \n",
       "v_Intercept_subj(high).7 -1.932482  0.104444 -2.159911 -1.997803 -1.923021   \n",
       "v_Intercept_subj(high).8  -1.93726  0.103167  -2.17164 -1.996201 -1.926672   \n",
       "v_Intercept_subj(high).9 -1.765546  0.092618 -1.932878  -1.82642 -1.774931   \n",
       "\n",
       "                               75q     97.5q    mc err  \n",
       "z_std                     0.165521  0.198529  0.000997  \n",
       "z_subj.0                  0.524261  0.540636  0.000602  \n",
       "z_subj.1                  0.475174  0.493847  0.000788  \n",
       "z_subj.2                  0.536549  0.551951  0.000726  \n",
       "z_subj.3                  0.554478    0.5732  0.000691  \n",
       "z_subj.4                  0.536063  0.554221  0.000643  \n",
       "z_subj.5                  0.566938   0.58437  0.000808  \n",
       "z_subj.6                   0.51188  0.532034  0.000713  \n",
       "z_subj.7                  0.483024  0.501168   0.00073  \n",
       "z_subj.8                  0.494706  0.513391  0.000777  \n",
       "z_subj.9                   0.64011  0.661067  0.000929  \n",
       "v_Intercept(high)        -1.816348 -1.740457  0.003367  \n",
       "v_Intercept(low)          2.185435  2.263962  0.004015  \n",
       "v_Intercept_std(high)     0.150339  0.264118  0.004854  \n",
       "v_Intercept_std(low)      0.142855  0.281003  0.005491  \n",
       "v_Intercept_subj(high).0  -1.83045 -1.708685  0.005045  \n",
       "v_Intercept_subj(high).1 -1.788356 -1.676289  0.004631  \n",
       "v_Intercept_subj(high).2 -1.783263 -1.661354  0.004157  \n",
       "v_Intercept_subj(high).3 -1.814767 -1.694395  0.004373  \n",
       "v_Intercept_subj(high).4 -1.736074 -1.610692  0.004896  \n",
       "v_Intercept_subj(high).5 -1.721493 -1.580278  0.005394  \n",
       "v_Intercept_subj(high).6 -1.844415 -1.750168  0.005198  \n",
       "v_Intercept_subj(high).7 -1.853874 -1.758545  0.007063  \n",
       "v_Intercept_subj(high).8 -1.864916 -1.762385  0.007038  \n",
       "v_Intercept_subj(high).9 -1.711732 -1.553556  0.005624  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_group_reg_depends[0].gen_stats()[25:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22ac67f-85f1-4d3b-a029-a11fac49d6c2",
   "metadata": {},
   "source": [
    "### Link Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e86817b5-b4f5-48fd-a3d7-fe52bf42ca4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate some data\n",
    "model = \"ddm_hddm_base\"\n",
    "n_samples = 1000\n",
    "\n",
    "data, parameter_dict = simulator_h_c(\n",
    "    n_subjects=1,\n",
    "    n_trials_per_subject=n_samples,\n",
    "    model=model,\n",
    "    p_outlier=0.00,\n",
    "    conditions=None,\n",
    "    depends_on=None,\n",
    "    regression_models=None,\n",
    "    regression_covariates=None,\n",
    "    group_only_regressors=False,\n",
    "    group_only=None,\n",
    "    fixed_at_default=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6d06115f-c50f-407f-a155-0938d4806eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'z': 0.6154312864537118,\n",
       " 'v': 0.5083726853982586,\n",
       " 't': 1.2055509268384164,\n",
       " 'a': 1.954380539357642}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameter_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "185ce3d9-1f5c-4931-a84a-a9c565a0bcfb",
   "metadata": {},
   "source": [
    "##### SOME NOTES:\n",
    "\n",
    "In principle you are free to choose link function as you please. \n",
    "\n",
    "The general model you will fit is of the following form (take the drift parameter as an example):\n",
    "\n",
    "$$ \\hat{v} = link(\\mathbf{X} \\beta) $$\n",
    "\n",
    "Using the **identity link** leaves us with,\n",
    "\n",
    "$$ \\hat{v} = \\mathbf{X} \\beta $$\n",
    "\n",
    "Using the **logistic link** leave us with,\n",
    "\n",
    "$$ \\hat{v} = \\frac{1}{1 + \\exp( - \\mathbf{X} \\beta)} $$\n",
    "\n",
    "The **identity function** will most often be the right choice."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74683876-9b2f-49a3-baef-451d3494055b",
   "metadata": {},
   "source": [
    "#### Example 1: Identity Link on z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5a6138b0-2ddd-487c-b022-36ddc109b4c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 1500 of 1500 complete in 47.1 sec"
     ]
    }
   ],
   "source": [
    "def identity_link(x):\n",
    "    return x\n",
    "\n",
    "\n",
    "def logit_link(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "reg_models = [\n",
    "    {\"model\": \"z ~ 1\", \"link_func\": identity_link},\n",
    "    {\"model\": \"v ~ 1\", \"link_func\": identity_link},\n",
    "]\n",
    "\n",
    "hddm_models_link_id = []\n",
    "n_models = 1\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_link_id.append(\n",
    "        hddm.HDDMRegressor(\n",
    "            data,\n",
    "            reg_models,\n",
    "            informative=True,\n",
    "            is_group_model=False,\n",
    "            group_only_regressors=True,\n",
    "            include=[\"v\", \"a\", \"z\", \"t\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 1500\n",
    "for i in range(1):\n",
    "    print(i)\n",
    "    hddm_models_link_id[i].sample(nmcmc, burn=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ce19c660-83f6-49fd-9db5-9ed5cee96749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>4.909676</td>\n",
       "      <td>0.32413</td>\n",
       "      <td>4.370016</td>\n",
       "      <td>4.64293</td>\n",
       "      <td>4.90955</td>\n",
       "      <td>5.108711</td>\n",
       "      <td>5.626281</td>\n",
       "      <td>0.029705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>1.370789</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>1.255865</td>\n",
       "      <td>1.336507</td>\n",
       "      <td>1.375113</td>\n",
       "      <td>1.409246</td>\n",
       "      <td>1.459022</td>\n",
       "      <td>0.004458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_Intercept</th>\n",
       "      <td>0.528905</td>\n",
       "      <td>0.031882</td>\n",
       "      <td>0.468648</td>\n",
       "      <td>0.506491</td>\n",
       "      <td>0.526541</td>\n",
       "      <td>0.549243</td>\n",
       "      <td>0.593577</td>\n",
       "      <td>0.002954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept</th>\n",
       "      <td>1.265397</td>\n",
       "      <td>0.050028</td>\n",
       "      <td>1.173632</td>\n",
       "      <td>1.227622</td>\n",
       "      <td>1.263576</td>\n",
       "      <td>1.301017</td>\n",
       "      <td>1.365643</td>\n",
       "      <td>0.003991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean       std      2.5q       25q       50q       75q  \\\n",
       "a            4.909676   0.32413  4.370016   4.64293   4.90955  5.108711   \n",
       "t            1.370789    0.0515  1.255865  1.336507  1.375113  1.409246   \n",
       "z_Intercept  0.528905  0.031882  0.468648  0.506491  0.526541  0.549243   \n",
       "v_Intercept  1.265397  0.050028  1.173632  1.227622  1.263576  1.301017   \n",
       "\n",
       "                97.5q    mc err  \n",
       "a            5.626281  0.029705  \n",
       "t            1.459022  0.004458  \n",
       "z_Intercept  0.593577  0.002954  \n",
       "v_Intercept  1.365643  0.003991  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_link_id[0].gen_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fac517be-0f2b-47f6-b852-925529117d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Posterior Predictive\n",
    "hddm.plotting.plot_posterior_predictive(\n",
    "    model=hddm_models_link_id[0],\n",
    "    columns=2,  # groupby = ['subj_idx'],\n",
    "    figsize=(8, 6),\n",
    "    value_range=np.arange(-5, 5, 0.05),\n",
    "    parameter_recovery_mode=False,\n",
    "    required_method=\"random\",\n",
    "    plot_func=hddm.plotting._plot_func_posterior_node_from_sim,\n",
    "    **{\n",
    "        \"alpha\": 0.01,\n",
    "        \"ylim\": 3,\n",
    "        \"bin_size\": 0.1,\n",
    "        \"add_posterior_mean_rts\": True,\n",
    "        \"add_posterior_uncertainty_rts\": True,\n",
    "        \"plot_likelihood_raw\": False,\n",
    "        \"samples\": 200,\n",
    "        \"legend_fontsize\": 7,\n",
    "        \"subplots_adjust\": {\"top\": 0.9, \"hspace\": 0.3, \"wspace\": 0.3},\n",
    "        \"posterior_uncertainty_color\": \"red\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7098e4c8-68ba-428f-8b5c-f9075660e41e",
   "metadata": {},
   "source": [
    "#### Example 2: Logit link on z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "594d14ce-63b7-486e-b42c-06a9edd7d06d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 1500 of 1500 complete in 46.4 sec"
     ]
    }
   ],
   "source": [
    "def identity_link(x):\n",
    "    return x\n",
    "\n",
    "\n",
    "def logit_link(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "reg_models = [\n",
    "    {\"model\": \"z ~ 1\", \"link_func\": logit_link},\n",
    "    {\"model\": \"v ~ 1\", \"link_func\": identity_link},\n",
    "]\n",
    "\n",
    "hddm_models_link_logit = []\n",
    "n_models = 1\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_link_logit.append(\n",
    "        hddm.HDDMRegressor(\n",
    "            data,\n",
    "            reg_models,\n",
    "            informative=True,\n",
    "            is_group_model=False,\n",
    "            group_only_regressors=True,\n",
    "            include=[\"v\", \"a\", \"z\", \"t\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 1500\n",
    "for i in range(1):\n",
    "    print(i)\n",
    "    hddm_models_link_logit[i].sample(nmcmc, burn=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "74486c05-195f-4919-bcef-c11e0be1d9ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.997498</td>\n",
       "      <td>0.034517</td>\n",
       "      <td>1.931216</td>\n",
       "      <td>1.973821</td>\n",
       "      <td>1.997247</td>\n",
       "      <td>2.020934</td>\n",
       "      <td>2.067506</td>\n",
       "      <td>0.001364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>1.210438</td>\n",
       "      <td>0.00742</td>\n",
       "      <td>1.194441</td>\n",
       "      <td>1.206324</td>\n",
       "      <td>1.210842</td>\n",
       "      <td>1.21552</td>\n",
       "      <td>1.224013</td>\n",
       "      <td>0.000434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_Intercept</th>\n",
       "      <td>0.624583</td>\n",
       "      <td>0.012586</td>\n",
       "      <td>0.597178</td>\n",
       "      <td>0.615603</td>\n",
       "      <td>0.624944</td>\n",
       "      <td>0.63359</td>\n",
       "      <td>0.647763</td>\n",
       "      <td>0.000777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept</th>\n",
       "      <td>0.519173</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>0.431535</td>\n",
       "      <td>0.487425</td>\n",
       "      <td>0.517122</td>\n",
       "      <td>0.551806</td>\n",
       "      <td>0.615781</td>\n",
       "      <td>0.002395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean       std      2.5q       25q       50q       75q  \\\n",
       "a            1.997498  0.034517  1.931216  1.973821  1.997247  2.020934   \n",
       "t            1.210438   0.00742  1.194441  1.206324  1.210842   1.21552   \n",
       "z_Intercept  0.624583  0.012586  0.597178  0.615603  0.624944   0.63359   \n",
       "v_Intercept  0.519173  0.046946  0.431535  0.487425  0.517122  0.551806   \n",
       "\n",
       "                97.5q    mc err  \n",
       "a            2.067506  0.001364  \n",
       "t            1.224013  0.000434  \n",
       "z_Intercept  0.647763  0.000777  \n",
       "v_Intercept  0.615781  0.002395  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_link_id[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a8fdf9-a4b1-438a-bd7f-866358ff39df",
   "metadata": {},
   "source": [
    "##### NOTES:\n",
    "\n",
    "When the **logit link** is used (any link), we have to transform the **z_Intercept** parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7bebbbd7-4fef-4d0b-afc5-959bf60da3e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed z\n",
      "0.6512602414581903\n",
      "Ground Truth z\n",
      "0.6154312864537118\n"
     ]
    }
   ],
   "source": [
    "z_intercept = hddm_models_link_id[0].gen_stats().loc[\"z_Intercept\", \"mean\"]\n",
    "\n",
    "print(\"Computed z\")\n",
    "print(1 / (1 + np.exp(-z_intercept)))\n",
    "\n",
    "print(\"Ground Truth z\")\n",
    "print(parameter_dict[\"z\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1e5c2b32-31a5-4c07-89b3-e3563fd7669a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6gAGaKFo0TV0kZIqDuoABmijSjDMIOQ6auoFHD1GksbdMEcIeNFGkyQDtdIoveZ8oTAzQRNFgNouRGzLFwYuE1AUM0ETRwouE1E0M0ETRxIuE1A08eoiIEhQDNFGkeTyBswiJuogpDqJoCM49M1hTF7AHTRQtLJZE3cQeNFE08SIhdQMDNFG0cNFY6ib+eyeKFqY4qJvYgyaKJqY4qBsYoImihSkO6iYGaKJocbvVSiqsxUFdwABNFE02W7z3gJIYAzRRtMgZhfI+UZgYoImihWsSUjcxQBNFC8uNUjcxQBNFE4fZUTcwQBNFC5e8om7q1r/3ZcuWobCwsMNt9u7di9mzZ2PcuHGYMGEC5s6di8OHD3fnZYmSg8cjUhxWKy8SUpd0OUC/+OKLWLp0aYfb1NTU4KabbsIJJ5yAl19+Gc899xxqamowc+ZMuFyurr40UWIz1oOWAZqoC8I+co4cOYL77rsPW7duxdChQzvc9oMPPkBLSwsee+wxpKamAgAWLVqEyZMnY9u2bZgwYULX9poo0VmtIkBzmB11Q9g96F27dsHhcKCkpASjRo3qcNsJEybg6aef9gdno7q6unBfmii5OJ2qN80cNHVB2D3oqVOnYurUqZ3aNi8vD3l5eQFtzz77LFJTUzF27NiQPzNt2rR2n6+iogK5ubmd31mieJBB2e1W6Q32oKkLYjoGaNWqVVi9ejXuvvtu9OvXL5YvTRQ7suds7DUzQFMXxOTqha7rePLJJ/HMM8/g1ltvxY033tjutuvXr2/3sY5610QJQ9NEDQ5NU+OgOVGFuiDqAbq1tRV/+MMf8O6772L+/Pm45ZZbov2SRPElR3B4vSowswdNXRD1AD1//nysW7cOTzzxBK644opovxxRYggeWuf1xmc/KKlFNEB7vV5UV1ejT58+sNvteOutt1BaWor58+fj/PPPx9GjR/3bym2IeiyvV/WcW1tVb5rTv6mTInqkVFRUYOLEiSgtLQUAvPvuuwCAxx9/HBMnTgz4ktsQ9ThyJRWvVwRjs1ndJwpDt3rQjz32WMD3eXl52LNnj//7lStXdufpiZKTzEF7PLxISN3COahEkSZHb7S2ciYhdQsDNFGkGReKZT1o6gYmxYgiTaY4jEGZAZq6gAGaKNKam9WK3jJQhwraRMfBAE0UaZoG2O2AyaRGcRhnFRJ1EnPQRNEgh9bJxWJbW+O7P5SUGKCJIk0udeX1ip40oNIbTHFQGBigiSLNWCxJDq/jRBXqAgZookiTFwT/9S/g6qtF265d8d0nSkoM0ESR5naLAO12A2+/LdoGD47vPlFSYoAmigabTYzi4IKx1A08eogiTS55pesqB63r8d0nSkoM0ESRJsc8M0BTNzFAE0Wa0ynyz7quRm4wQFMXMEATRZocZgeoW6IuYIAmijTjZBSWGaVuYIAmigaZ2pABmjMIqQs4tYko0kJN6WYOmrqAPWiiSJOlRgFeJKRuYYAmijS5FqH8IuoiBmiiSDNeGGQPmrqBAZooGuQUb1kPmgGauoDnX0SRJi8SypmEsrqdvE/USexBE0WaDMiAKtgPiF617FETdQIDNFGk+WpxFNY8iborRZOjaTmK47tXlIQYoIkirbUV8HhQp/VByV9Fj7kgM5NLXlHYGKCJosFmA2AKTHEADNAUFl4kJIo0XfcF4hAjNxigKQzsQRNFmjGVYQzInLRCYWKAJoo0/4oqMAyr4zhoCh8DNFGk6bpvTUIE1oPmRUIKE8+5iKIhVDpDTlYh6iQGaKJI83pDT0gxm9mDprAwxUEUaXLRWCCwJ82LhBQmBmiiSPMvFmtqu/wVUxwUBgZookjzen2BWFcBWYeqcEfUSTxiiCLNXyzJFDiKgz1oChMDNFGkyWF2QGCKgz1oChOPGKJI83jUKA7/aA6d46ApbAzQRJEWcskrcBQHhY1HDFGkffQR8MtfAmZfDlqmO2TPWvak2Zum42CAJoo0txt4+22gb9/Adk0Tj3G1b+okpjiIIk3XAadTpDWcTtVuNnMUB4WFAZooGux2keIwFuznVG8KEwM0UaTJVAYQ2GM2ju4g6gQGaKJI86+oYmo7ooMpDgoDAzRRNNjtgMWsUhwmMMVBYWOAJoo03bB6SnBAZoCmMDBAE0WaHMUBqIAsl79iDprCwABNFGlyEorJMN7ZBFGLgz1oCgMDNFGk6boaZmcMyFzyisLEAE0ULcYetCzezxQHhaFb802XLVuGwsLCDrepqanB7373O4wdOxZjx47FAw88gObm5u68LFHis1pFWkOSozjYg6YwdDlAv/jii1i6dOlxt5s7dy4OHTrk3/7f//43Hnnkka6+LFHi84+DRmDdDQ6zozCFneI4cuQI7rvvPmzduhVDhw7tcNvPP/8cn332GUpLSzF8+HAAwIIFCzBz5kzcfffdOOmkk7q210SJTgZiY0B2OgGePVIYwu5B79q1Cw6HAyUlJRg1alSH227ZsgUnnniiPzgDwPnnnw+TyYStW7eGv7dEyUDTfIHYFNhuszHFQWEJuwc9depUTJ06tVPbHjlyBLm5uQFtNpsNWVlZqKioCPkz06ZNa/f5Kioq2jwfUaIpbFqOuhmZcKS3Bl4kBBigKSxRHcXR0tICm3HRTJ/U1FS4XK5ovjRR3NTpmSh5ww08+hagna0e8HgCy48SHUdUA7Tdboc7xLAil8uF9PT0kD+zfv36dp+vo941UcKQFwlTU1WbHMXhdAbmp1m4nzoQ1aMjJycHVVVVAW1utxu1tbW8QEg9m90OpKcbRnGY1ExCrqhCnRTVo2Ts2LGorKzEgQMH/G2bNm0CAJx77rnRfGmi+Ao1IaW5WfWguSYhdUJEA7TX68XRo0fh9OXZRo0ahXPPPRd33XUXvvzyS2zcuBEPPfQQrrrqKvagqQfTRYA2Xn+RAzq4JiGFIaJHSUVFBSZOnIjS0lIAgMlkwp///Gfk5eXhhhtuwJ133omLLroIDz/8cCRflijxWK1ARoahwbf8FXvNFIZuXSR87LHHAr7Py8vDnj17Atr69evXqRmHRD2GDtF7Nq5HCIigDYSexEIUAoslEUVDqODr8YgRHkxvUCcxQBNFnN42zyyH2Xm9arIKJ63QcTBAE0VDqDSGHLlh5ceOOodHClGk6RDD6UIFYq9XDcFjbWg6DgZoomjIyAAyMwPbZMCWqQ+mOOg4GKCJosWQh3aYGlAwXYNj9w0oZoqDOomXk4miIWgUR7HjdpT8Hahzp8VphygZMUATRUObURwmkXPWOfaZOo8BmigagsdBm0xtJ64QHQeTYUQRp6sLgMZA7XaLER5EncQATRQNVquY7i3THDLF4fWqov0s3k/HwQBNFA3BOWizWdSHBpjqoE5jDpoo0nS0HeOckgIUFAA1NXHZJUpODNBE0SJXUNE0YOJE4O23xSgOt1t9EXWAKQ6iaAjOQVssaiZhiIWUiUJhD5qIKEGxB00UDcHD7HSdw+wobAzQRNEQKsXBQv0UJgZoolgwm7nkFYWNAZooGkKVEm1sRMAsQ47ioOPgORdRNFitgQX7Qy0iywBNx8EeNFEsGFMcHGZHncQeNFGsOJ0cxUFhYQ+aKOL0thcA5agO42O8SEjHwQBNFA2aJtIaMgibTOriIIfbUScxQBNFg9kcOA5a5p91sAdNncYATRQNweVGLRZf/WedPWjqNB4pRNEQ3DtOTRXD7HiRkMLAHjRRtBh7yikpKs0hc9GhJrMQGbAHTRQNoVZU8XgAk2EbBmg6DvagiWLBODnFyo8ddQ570ESxIAO0MQctV1vhaA5qBwM0UTQE19mw2QKH18kvjuigDvBciyga/DMHfex2ldpgUKZOYoAmihZjIDbmnTmKgzqJ/8qJoiF4FEeoC4MM0HQc7EETxYLV6stBG2YS8uIgHQcDNFEs2GwqJy0DM3vQdBwM0ETREKp3HJx7djpjtz+UlBigiaIhVA5aBm3ZziWv6DgYoIliwV961MQlr6jTGKCJYsFqVT1nDrOjTmKAJoqG4By0LJZkfIwpDjoOBmiiaAjOQdtsaiy0vOUwOzoOTlQhigVjwDbeZ7Ek6gB70ESxIofVNTcH3jJAUzsYoIliwW4XXyYTkJ6u2lk4iTrAAE0UDe32inWO4qBOY4Amiobgi4SACMg6VGqjqSnmu0XJhQGaKBqCe9ByHLQxxcHcMx0HAzRRNAT3nn3fO0z1KPiZRdz/7hoUPxrrHaNkEvYVCk3TsHTpUkyaNAmjRo3CzTffjAMHDrS7/dGjR3H33Xdj3LhxGDduHO644w5UVlZ2a6eJko4vQBdn/H8oeaUBJa80oK6FU76pY2EH6GXLlmHNmjVYuHAhXnvtNZhMJsyaNQvudmZF3XXXXaioqMALL7yAF154AZWVlbjtttu6veNESUXmpM1mkeJIT0fgCrJEbYUVoN1uN1auXIk5c+Zg8uTJyM/Px5IlS3DkyBGsW7euzfb19fXYvHkzZs2ahREjRmDEiBGYPXs2du3ahZqamoj9EkQJJ1R+2VgH2uMBNAZo6lhYAXr37t1oamrC+PHj/W2ZmZkYMWIENm/e3Gb71NRUpKen4+2330ZjYyMaGxvx97//HUOGDIHD4ej+3hMlquBRHPK+yaQe03XOJKQOhXWRUOaOc3NzA9oHDBiAioqKNtunpqbi0UcfxYIFCzBmzBiYTCaceOKJePnll2FuZ4D+tGnT2n39ioqKNq9NlBTMZlEcyWRSbbovMDNAUzvC6kG3tLQAAGxB9WxTU1PhcrnabK/rOvbs2YNzzjkHr7zyCl566SUMGjQIv/3tb9HY2NiN3SZKMna7qgnt70GDMwmpQ2H1oO12OwCRi5b3AcDlciEtLa3N9u+99x5Wr16Nf/7zn8jIyAAALF++HD/+8Y/x5ptv4oYbbmjzM+vXr2/39TvqXRMlNLNZ9JSNPWgwxUEdC+vft0wvVFVVBbRXVVUhJyenzfZbt27F0KFD/cEZABwOB4YOHYr9+/d3YXeJkpQsMRqQ4oAIzpzyTe0IK0Dn5+cjIyMDmzZt8rfV19fjq6++wpgxY9psn5ubiwMHDgSkP1paWlBeXo7Bgwd3Y7eJkozVKr5MJnUfUD1rohDCCtA2mw0zZszA4sWLsX79euzevRt33XUXcnJycPHFF8Pr9eLo0aNw+soqXnXVVQCAO++8E7t37/Zvb7PZcPXVV0f+tyFKZJomRm5IJjAHTR0K++iYO3currnmGtx///247rrrYLFY8Pzzz8Nms6GiogITJ05EaWkpADG6Y/Xq1dB1HTfccANuuukmpKSk4NVXX0VmZmbEfxmihGUMxG63+NKYg6aOhV2Lw2KxoKioCEVFRW0ey8vLw549ewLahg8fjuXLl3d9D4l6AjlyIzUV+PnPRVv1HeKWAZrawWJJRLF0wQXAK6+I+2lrxS0DNLWDAZoolnQdkHMAOIqDjoMBmiiWUlLEpBUA/nHQ7RQaI+IlZKJYCh61YWPJUWofe9BEsWQc9yxTHMxBUzsYoIliRdMAi6VtL5oBmtrBAE0UC3KYXXCA5kQV6gADNFEs6bph1IYOOJ3iiygE/vsmiqWAanYQFwmZ4qB2sAdNFEsWS+DIDaY4qAMM0ESxJlMaOlMc1DEGaKJYsloNPWhf6VHOJKR2MEATxYpcUcWY1rDyI0jtYwKMKBbkMLvgi4ScqEId4L9voliRgdg4zM7YThSEAZooFjhRhbqAAZooloJz0ExxUAcYoIliLbi8KAM0tYMBmiiWglMcLNhPHWACjCiWgnPOHGZHHeDRQRRLZrNhySsdqK0VX0QhMEATxVKfPoEzCdPTueQVtYsBmiiW0tPFFwCYwCWvqEMM0ESxIi8GNjerNg6zow4wQBPFgrwYmJmpetAAR3FQhziKgyieOIqDOsCjgyjWjPWf3W5eJKR2MUATxZLdrnLOOkR6w5iTJjJggCaKpeBRHMaATRSEAZoo1owpDl4kpA4wQBPFkt0eON27uZkpDmoXAzRRLNlsanKKySSG3RG1g8PsiOLJ7Qa83njvBSUo9qCJYq2+Xtz6VrxigKb2MEATxZJxJqEJYqKKrnf4I9R7MUATxZLVGjh7kGsSUgd4dBDFk9PJYXbULvagiWLNPzHFJEZ0MMVB7bY7wxYAACAASURBVGCAJools7ltWoMBmtrBAE0Ua8biSKwHTR1ggCaKJZst8CKhpnGYHbWLAZoolowpDg6zo+NggCaKNf+q3hDpDo7ioHYwQBPFUkZG4EQVQARomYfmuGgy4NFAFEsBAdikCicxMFMI7EETxZIxB202AVdeCezapUZzMFCTAY8GoljzeMRX377Aa68BLhcDM4XEo4IolmQtDqsVjlQXCq5NR2HN0njvFSUoBmiiOCme+CxK3tZQp/WJ965QgmKAJooXk8m33JUuhtsZZxgSgQGaKH5sNrFGISDSHpzyTUEYoInixWz2Tfs28SIhhRT2UaFpGpYuXYpJkyZh1KhRuPnmm3HgwIF2t29tbcUTTzyBSZMmYfTo0ZgxYwa+/vrrbu00EVFvEHaAXrZsGdasWYOFCxfitddeg8lkwqxZs+BuJ3/28MMP44033sB///d/480330RWVhZmzZqFhoaGbu88UVKSw+wAkYPWmYOm0MIK0G63GytXrsScOXMwefJk5OfnY8mSJThy5AjWrVvXZvtDhw7hjTfewB//+EdMmTIFw4cPx//8z//AZrNh586dEfsliJKGYZgdUlPFtG8T1IxCIoOwAvTu3bvR1NSE8ePH+9syMzMxYsQIbN68uc32n3zyCTIzM3HRRRcFbP/hhx9iwoQJ3dhtoiRmrAHt8YiiSawLTSGENdW7srISAJCbmxvQPmDAAFRUVLTZfv/+/Tj55JOxdu1arFixAkeOHMGIESNwzz33YPjw4SFfY9q0ae2+fkVFRZvXJkoqxqneFotYkxC+cqMM0BQkrB50S0sLAMAWdDqWmpoKl8vVZvvGxkYcPHgQy5Ytw913341nnnkGVqsV119/PX744Ydu7DZRD2Cziep2AEdxUEhh9aDtvjGbbrfbfx8AXC4X0tLS2myfkpKChoYGLFmyxN9jXrJkCSZPnoy//e1vmDlzZpufWb9+fbuv31HvmigpyYuFTHFQCGH925bphaqqqoD2qqoq5OTktNk+JycHVqs1IJ1ht9tx8skno7y8vCv7S9RzmM0iKMvC/U5nvPeIEkxYATo/Px8ZGRnYtGmTv62+vh5fffUVxowZ02b7MWPGwOPxYMeOHf42p9OJQ4cOYfDgwd3YbaIewGIRozlMUDMKiQzCSnHYbDbMmDEDixcvRnZ2NgYNGoRFixYhJycHF198MbxeL6qrq9GnTx/Y7XaMGTMGF1xwAX7/+99jwYIFyMrKwtKlS2GxWHDllVdG63ciSg4mk0prMMVBIYR9ZWLu3Lm45pprcP/99+O6666DxWLB888/D5vNhoqKCkycOBGlpaX+7Z966imcf/75uP3223HNNdegsbERq1atQnZ2dkR/EaKkIYOxySTSGrqhncgg7BVVLBYLioqKUFRU1OaxvLw87NmzJ6AtIyMDDz/8MB5++OEu7yRRj2EcZpeertYn5CgOCoFLXhHFmrGnrGkAdKY4KCT+2yaKJWNP2WwGGhvVTEI55I7Ihz1oolgypjiystREFYABmtpggCaKF6vVsMK3WQRoY5qDeelej0cAUbzIiSqACM5ud2APm3o9HglE8WINOoFlPWgKwgBNFC/GAM160BQCAzRRvBgXivV4xKQVudoKLxgSGKCJ4sdmU/lmpxOor1c5aI6JJnAUB1H8yKWvTGBdaAqJAZoonuSFQTmKw1g8iXo9BmiiWJPB12ZT9aCbm0WKI3gb6tUYoIliyZjCSE8XQdoEIDMzcDsGaAIDNFH8GC8GymJJzEGTAY8GoniRw+x0AFVVwJEj8d4jSjAM0ETxYrP5UhwmoH9/pjWoDaY4iOJJBmWrFdB1juKgAAzQRPFiNvtW8tbFKA6XK3BmIfV6DNBE8eJf8sqkhtwFF1CiXo1HA1G8mSB600xxUBAGaKJ4crvFKI7qaqClRc0sdDrjuluUGBigieJFFksyQaQ2WltV2VHj6iocG91r8Z0nihd/sSSTmEmo64ErqjAw93rsQRMlAqdTpDdkasPp5OxCYg+aKP5MIhC3tqoetMfDwEzsQRPFnQm+4Xbg0lcUgAGaKJ40DTCZgYICYMcODrOjADyHIooXWc2uXzawZo2YSeh0iq/m5njvHSUA9qCJ4kWO4jCbxZJXJpNKdRCBPWii+ApOZcgVvY0rfFOvxR40UbzI0RoAUFsrgnVjo/i+upqrexMDNFHcyHrQFotIcZjNQFaWepzD7Ho9BmiieDOZVBW74CWw2IPu1RigieJJLnl17JhId1RVifbDh8VjbndgkGavuldhgCaKFzlr0GIRtTjk0leACNayVy2DMnvTvQ4DNFG8GVMc8laO4OAojl6NAZooETidopqdnKDS3MxRHMQATRR3FrOqwSF70F4v883EAE0UdyazqgvNYklkwABNFE8yhVFfL+7LFIfLJUZwyCWwqFdigCaKF5nCkDU5dF0F5KYmcYGQRZN6NQZoongxDrNLT1dLXwEsnEQAWCyJKDEEr+JtMnEmIbEHTRRXcs3BxkaR4qitFe0tLeIxjoPu1RigieJFpjhsNjGDMCUFyM5Wj1v58ezteAQQJQKZ4mCPmQwYoInizWIOHG4HiNEcjY2iLrQc2aFpgN0en32kuGCAJoo3i0UE3tRU4IYbRNtXX4lRHJqmJq8EX0ikHo8BmijuTCIXPW6cWDwWAAYP5lRvYoAmSggyjXHwoLhtbRUpDrkEFvVKDNBE8Wb21eLo2xfIyVHtMsVhXGWFepWwz6E0TcPSpUsxadIkjBo1CjfffDMOHDjQqZ995513cPrpp6O8vDzsHSXqsUxQQdpuVxcC5TA8+UW9Ttjv+rJly7BmzRosXLgQr732GkwmE2bNmgX3cYq6fP/993jkkUe6vKNEPZqmqYkqtbWi3Ghjo7gviyaxcFKvE1aAdrvdWLlyJebMmYPJkycjPz8fS5YswZEjR7Bu3bp2f07TNBQVFeHMM8/s9g4T9TwmMf7ZbhdpDVmDQ97K1b+p1wkrQO/evRtNTU0YP368vy0zMxMjRozA5s2b2/255cuXo7W1FbfeemvX95Sop5KzCY1fsp16tbAuElZWVgIAcnNzA9oHDBiAioqKkD/z5ZdfYuXKlXjjjTdw5MiRLu4mUQ8VfOFPlhfVNJHiqK9Xsws5y7DXCStAt7S0AABsQadbqampqKura7N9c3Mz5s2bh3nz5mHIkCGdCtDTpk1r97GKioo2/xyIkpbsIZsANDej8L1foe7LFACAo3EZiu12EahZk6PXCuudt/uuLrvdbv99AHC5XEhLS2uz/cKFCzFkyBBce+213dxNoh7MYgHSU1Hn7YOSUhGgC2yZDMwUXoCWvdeqqiqccsop/vaqqirk5+e32f7NN9+EzWbDOeecAwDwer0AgJ/+9KcoKCjAggUL2vzM+vXr2339jnrXREnN4xG9ZeNIDY+HaY1eLqwAnZ+fj4yMDGzatMkfoOvr6/HVV19hxowZbbZfu3ZtwPfbt29HUVERVqxYgeHDh3djt4l6EP+isQCqqkSbpotZhRUVqgYHa3H0OmEFaJvNhhkzZmDx4sXIzs7GoEGDsGjRIuTk5ODiiy+G1+tFdXU1+vTpA7vdjsGDBwf8vLzIOHDgQPTr1y9yvwVRT2C3q5mE5h3AsGGqkBL1SmGP45k7dy6uueYa3H///bjuuutgsVjw/PPPw2azoaKiAhMnTkRpaWk09pWoZ5M9aeaeySfsI8FisaCoqAhFRUVtHsvLy8OePXva/dlx48Z1+DhRryVzzbIeNAAcPgwcPQocO6a+HzBA3OfElV6BI+GJ4s3kKzeakgJkZIgvk0kF4/79xRfAwNzL8FyKKN5ksSSzyTB70CRSHbrOana9GAM0USIIXvIKOlBZKWYTytmF8pZ6DQZoongzpjgyMwEADksjCmadBMfOOSiWRZMkY41ogDU7ejC+s0QJqLjvHSh5x4S61vTQ9aBZI7pX4DtMlEhk3WerFbj8cuCHarX0FZe/6nUYoIkShXEc9KRJwOrVIpUhV1lxu8X3clp4cKqDehzmoIkShckwisNuB7KyxAgP48QV+bi8ZYDu0RigiRKBDLRywoqui1EbOlQNjuZmFlDqZRigieLM4QAKrgQcmbpqtFp9gVkPHIIXXPGOejQGaKI4Ky72pTb21wBWh2hMSwOyswGY1NqEAGcS9jIM0ESREI1ccPBz8sJgr8MATRQpkRiXbJwtWFsLQPfdAvj+e6C6WtSMlnU6GKx7NAZookSRnq7SGX36qBRHVpbapn9/UdWOk1R6BQZoomQhCyfJcdAAe9A9HAM0USKRswU1TfSUdQ0oLxdtDQ0iBdLYyB50L8EATZQoMjLU8laDBonlr0w71DJYsl409RoM0ESJwlgAyXifS2D1WnzniRKJMadcWQnAt7o3ANTViRTHsWNqtEdjowrgDOQ9Dt9RokRh7DUPHCiG0pnM4r583GoVMwnlaA8ZoDn9u0digCZKRKFSHGYzZxL2MgzQRJEQqeFuxueRRZLkbWurmLRSXa22c7tVASWmOHocvqNEkaBp3R/6Zuw1p6er+8biSBkZgd97PGI749ho+VyU9BigiSIlkkHRZhNfFjMwY4Zo27UrMN0R/LqsEd3jMEATJSqPB+ibDbzxhvh++HC1JJbEAko9GgM0USKRgdZqBZqb4UhpRsGVosnxw/+iuLlZ5KDr60VjdTWQl8ca0T0UAzRRojDmoLOygMxMFF9SDDw7EQBQkNZHtJvNQGam+hmO7OixGKCJIiGaaQbjGGdNE0WTjI9xGaweiwGaKFK6c5EwVHD3eMRCstXV4ntdF/dlmgMQswrNZuageygGaKJ4CxXYrVbxlZEhakADAEyiRrTd7qsV3cHPU4/Ad5YokZlMgd8HB2OO4ujR2IMmioRoBEmPR6U1AFEb+ptvxPdyGSw5q9DtDiziz151j8AATRQpkQyKMsWRnq5SHCYzcNppKPx4Nur+SyyD5aieiOL/Cpq8wt50j8EATZRk6tx2lJSI+wXncohdT8YATRQJ0RjqJofUHTvma/Ddb/WI5bAAoKVZFFOSBZWoR2GAJoqUSFaTk5NW+vb1r+rtsDaj4Kxv4WiuAAac7duwUS2TRT0OAzRRIpO5aADFP38beOklYPBgwHpVnHeMYoEBmjrGEpadE41RHHJkhkyduN1iGSyvV9XekI8Hj+KgHoEBmo6PM9U6J9LlRuUSVzK/bLWKdIauqwDt9QVopzNwFId8v/hPNakxQBMlIuNMwowM0ZaeroolyTYTQueg+U+1R2CAJkp0MtDKIkm6rtIeOjiTsAdjgCYl1Ew0eT84APDUOVC0htmZzUB5ufje7Qb27RO3u3eLNlerWNn72DGV9nC7ObKjh2CApkAyGIealcaZah2LxjC7IUOAU04RbQMHAsOGifz0WWf5ttuuakPLutAcE91jMEATJapQ/wjbjNTQmeLowXieSh2Ta+BxSaWOydl8keq9ms3ib242i+Wt6utFEK6uFsPsysrEV4tT3O7cqd6nxka+Zz0Ee9B0fDYbV+zojEjmfe128TVggEph9O8vvqxW4LTTRJt1s7ivaSrFomniPWOqI+kxQBMlG5sNKCgQ92vnqBQHL9z2OAzQ1JZxVprTKXpybre6CCV7aKREo2CRDLyy9nNzs0hnnHsu8Nxzou2kTSLt0dSkznJ4ttNjMEBTaMYALHtm8hSauc02Cn83AHVaKgDAgfruP6EcxZGV5S+WhBEjgFNPBRwO1WaCWP7KZIrsKBJKCHxHiSKgrsGMkg99y1NZbwQwI3Yvbja3XRqLegQGaFJkWqO5WfWaGxvFRSqnM/AUmimOQJoWvRSHfF88HjEhxeMRRZMAQNOB/ftFe2OjaKuuFmOmOWEl6TFAUyCzGYW3ZaDOKT7YDms/FL/lO0yY4uiYDIaR6M3KFIf8AkQqIysroEY0TCYRjAFVn0OO6ODY6KQX9mVfTdOwdOlSTJo0CaNGjcLNN9+MAwcOtLv93r17MXv2bIwbNw4TJkzA3LlzcViuBkEJqa7ehJISoKQEqGvgqXPCkAWUUlLUMDwTxNnM8Vb/pqQU9ru4bNkyrFmzBgsXLsRrr70Gk8mEWbNmwR2iV1VTU4ObbroJJ5xwAl5++WU899xzqKmpwcyZM+FyuSLyC1A3GU+jPR61zJK/XQ+sN8xZa6HJlbWjfXYh35/mZvGlQ0xicTpVW3Nz2/eJ711SCitAu91urFy5EnPmzMHkyZORn5+PJUuW4MiRI1i3bl2b7T/44AO0tLTgsccew49+9COcddZZWLRoEb799lts27YtYr8EdZM8jfbnOr1q2JjXq2a1GQM5BfJ6VcGkSF2wM14TkNcF5G1jo/gym4Ff/AL46CP/doX3D0ZBAVA4Oy3w+dirTjphvWO7d+9GU1MTxo8f72/LzMzEiBEjsHnz5jbbT5gwAU8//TRSU1PbPFZXV9eF3aWokx/i9HTxZULgMDsO5Wqf/28WwRx0RoZ63gEDxPf9+on7AwaI+++/L4K5b6ZhXYsNJe+amZ7qAcL6tFX6rhzn5uYGtA8YMAAVFRVtts/Ly0NeXl5A27PPPovU1FSMHTs25GtMmzat3devqKho89rUTcZTXmOKgxKbP5Wiq7Mb4zJYmibSH5TUwgrQLS0tAABb0BCr1NTUTvWIV61ahdWrV+MPf/gD+vXrF85LUzTJHnJzs+ghu93iPgA4XSLHWVUFyH+2TieXVArmdouhbpFkHF5nbLNa/cPsHKYGFFzeCkf1/6JYvmdul29YpCHnzKngSSmsAG33DSNyu93++wDgcrmQlpbW3o9B13U8+eSTeOaZZ3DrrbfixhtvbHfb9evXt/tYR71ripD0dABO3y0ANKliPfI9D85Zk9C/v7i1WLr/XDKY2mwqrSTXKTzlFH+N6OKLHwZWTkFBaqYaeoca3/vXyBreSS6sf6kyvVBVVRXQXlVVhZycnJA/09raiqKiIixfvhzz58/H3Xff3cVdJaKQGHx7rLB60Pn5+cjIyMCmTZtwiu8/eH19Pb766ivMmBF6auv8+fOxbt06PPHEE7jiiiu6v8cUWU6n6qFVVopessulZsW5XWJm2r59qsRlVZXoLfK0WWltVUWNIs2Y4mhuFumU6mr1WGWlGA65b59vmxbxuEsDnIbJRRkZfM+STFgB2mazYcaMGVi8eDGys7MxaNAgLFq0CDk5Obj44ovh9XpRXV2NPn36wG6346233kJpaSnmz5+P888/H0ePHvU/l9yGEoDxfcjKAtASOCsuO1sEcXkKX1nJFEcoMsUQiRSHZLWqgJqZqUZzZGeLtr59xUxC85diOSwAMH3le/yYeh9l8X++Z0kl7H+lc+fOxTXXXIP7778f1113HSwWC55//nnYbDZUVFRg4sSJKC0tBQC8++67AIDHH38cEydODPiS2xARUWhhD2q1WCwoKipCUVFRm8fy8vKwZ88e//crV67s3t5R9MjT5uZmVfjo4EEgJwdoqAcO6+rx6mqR1pDFeOrr1aQMjosWnC1q9e1IM/Z6GxvF312moLxeYNs2sY2c/NWiibOcJhdQ73tva2tFisPpDF3Xm2mPhMRPV29mHB0g5eQA5gZVgMeyT5wu2+2qGI/ZLH6WMwoDyWGIkV7dW8rMFO9DZqZ6z4YMEQX8ze+LWwAwfy7eRxxUI3BqawNHhgAiWDPtkdAYoImSkQy27Pn2aAzQPZmxZxT8QZajN2prVS/r4EFxatzSImoMA2LV6OpqcfouRynImsTsQSutnuilOEKR6SZNE+knkwmYOlW0HbsHOHxYpKfkdseOiYuLjY2BFw5tNo7sSGAM0D1dR6ew8oNqHMUxYACAo+LUGQDMX6vRCb4UR+HiUahbYYXjhFQUvxqVvU5Cui+tgOjn5TMyVLppyBBgwAA4Bp2AgvQPAAAO0998Kap6NeFI08T7bAzG8j5THAmLAZoChepJySWVfIGnrtmKkg1AweUsxhNABuY4LD9VfNFfgBcuFN/k3QHg5+J+e+8nJQUG6N7OuBp1VZVKX8hTY49XpD0aGlSbu9VXj8MEHkI+Xk2MbokV+V5Iuq5SUF6veC+9nsDROm63+DlZVEnWVPF4AoM2A3jC4KerJzMW25G9O/lhlBXQ5JV8QN33ets+h3FNQqdT/LzHAqBtKdleqaN8fyTJGYEyxZGeLv4xmEyB1ezcbjG7Uf7TOHxYtNfXq32trxcjdIxrFxqvKzBQxx0DdE8X6mq/DNbBw67MZvGBNe1TAcBkErnV1FTDOnhVvguLTVHf/aTxww/AjAfF/RD1zyNCFksyDovMyBDvRUaG7/oBxEzGvDzAdEzNOAREEDabA6852GxtF7tlXjphMECTIpe60qE+oLquVlbxt8VtDxOXpgFvvy3u/+pX8dsH433j+yhH3bjdgauzU0JjgO5tZIqjtlb0nr75Rk2wkDMGW91AWZloc7uB3buBI0dUMR6XS/x8a3x+hYSk66oedCwuEsrUhaaJ19V14KuvRFtrqxgy6Xb760Zjzx7xM7t3q0lIO3eK976+Xg21pITCAN0byRSHTGPID2xqqkhnWA+qynX164EHnxQf7FNPFW2mnb50R5SqtyUrmWI44YTovk5mpgqoQ4aIIlb5+cBZZ6nXnzsXqJ8DDDxf/Vx2tvin7KtEiY0bxfPE8uImhYVXAXo742rPcqmr2jqgoEB8AUBJSfRXq+4J4r1qtnz9cePEe6YxF5Xs2INOZsHBIPiquxypUV+vesv19WI7ObNs715xhR8Qtzt3iuFZD/oueG3WgS++QOGRRai7UJzCO9xHxUzD5lQAWSCIf25yIYtYpDhCFVCSsz8bG4Evv4RDr0HBReIsx1F+KYp/WynSVHI/Dx8W29bWqkk2LICVUPhOJLvjLWkkr/gbl6uS92XQljnoL74A7r0XsMwBxozx/dwOYPTZqLNUo6TfzaLt8A5gyHcA2i4U3KvJICf/rtEilxwD1AiO/HxVDzovDxg5EsVZFwMfHQEAFAw+QeyfcbSHsQCWcRgmJQwGaFJaW8Wpce5nbR878UTxGKACOhFFFQN0T9bYKHrW1dWqp3TsmJjcUFYmLg7u22eoI9wCfPIJ4IZqc2niYpLLGrhdWRmHQRtpmhpFEe3XAQKvCcg2maryeMRFXbcb+PJL0dbiBMrLUfjMBNS9LS4KOirPRfEvfSN35FhoWR+cBZQSAgN0T5eR0XZyQkaGOKXNywNSUlQd4dRUYOJEIP1z4NxzRFvaLmD8meL23DNFW0qKb0RHWUx/lYQ3YoS4bWcB5W6TAdNuVykJOfkkL0+Nxjn5ZJHysNmAkSNFm2UzkJeHOk8tSjaIESAFw9LUcmby+Ghu5kSVBMIATdRTpaSokTi1c+K7L9QlDNDJzNjLkVffjaemsmaGsUBOZaW4SPT558Do0eL7b75Rz7F7N6B51UQVzSvqHHsNowQ8HvG4yxk4Tbg3LwKs62pSSKwFpz28XpG6OvVUNRrn/46K97a5BdiyRbS1+Ca5HDum3sfgad8UVwzQyS64Cllw7tBmC8xXOp1ikomuiw9wRoaagGK3i1Nj+y51IdC2X5w62xrUBAebzTdi4GsVlHv9B1tXoyOileIIRY7oMKY9Bg0S79/JJ6vROOb3xeQj63Zg9NmizfSFmrzSm/+5JjAG6N7CWGDHahVjdc1mYPNm4KqrArcxmdQHVm4HU9t/BhQonstQGYfeWa3ivQwej202AyYY6laD72OCY4DuyWSBHFkLWLYBqp6GywWsWiXa5swR28oCSYCh4I4eWECptlakP1h4R4n330C+P7KMrNcrRvAA4n1sbgaO/QBcfrloq75LbBeqRjQvEiYEBuhE0lFN4ePMGiy8yYK6BsCRYULxKl+gqK8XH7hjx8SHExD55v37RfGj6moU1i5F3c9TAACOslkolrPMZPF3E8SHXNPVkC0A+NnPgMp7AY+vZofTGf0JGolM01WOPtYz8WRaSxbxN5vFdQOz2f++O0z1KLjUBYdeo/4hD9oihtgdPKj+IdfWimAt1yukuGKATjQdDXHqYNZgXZ0JJe8CBT9FYL3njAyRrpC1nO12UWAnLQ0YNgx1KEPJelHcp2BIlnjshO9UHvUEryjG06cFGH2yaLvkEuCllwD7+yoVErzCR28kc/myql0syNRGVpYqoHTWWWJfcnL81xKKB90DZJ0J5O4E+vuqEsIkrivImtKAykez9kpCYAKqt9L18E9jjUWVeAqcXCZNEjNBJ06M955QGNiDTkZyaSpjESSnDlS7gKYUoFoUai68LQN1ac3AgeHAYjl7bByKL98HvPcecOGFgP6wGmYnh9TpUAV1dN1XjtJQ79jjEbPmNE1sD4jH+vePya+fkHQ9tmcRwcX5gcCRNJWVol2mperrgc8+E+/rxo2izesVxbG+/VblqsvKxPtYWalKzgJi9qkxx24sqsTiSlHDv2yystvFhy493dfQ5FuuqsG/zFFdSzNKPkkHHvs7cM89AICCH2WIIXIWC/Dpp0DeNvVBtH8tTokdPwAD+om29B/E6W+6GxjgC8B5eeI02lSuhuPF8rQ+Ucl/lrEslmS1ivvp6SpnPGSISG+MHatmEp56KnD++cDQocD48aLN8n/ifUxNVUtj2WziZysr1bElr1/I1wNUgI73hdEejikOIqIExR50Igm1CjcgekhyqSpArcJsXF/O5RIpjrpmYN9R0VbXAuz3zQCUhY6avMCGDSg89v+jblI1HKj1nwY7bB4UXNYKR7pHrbKhQ43ikCM7WlvFabLXq9Ij33wjZib2Wnr8zyKMF/aMo3bk97t3i/d1507RpumirbxcpUL+9S/RA//sM3ELiPd94ECRQjH2quVx2V6KI7h3zVRI2PgXSzTBkx2MucbgoB3clp0NoEXVBTZ9JT5kffqogkgp24EpY1FnWouSj7OBO/4OjHwSAFB872rg+nOBfU1A5klin0/VYwAAG9VJREFU+7RmIDsdSGsEsnyn7qNGidNk0z9UeuSLLyLy6yc1mYOPx2m/cUhcVpb4Bz56tHp/TjtNzBKV6SkASKkA5s8XQVymQt58UxR92rhRjfyR61c6nYHHm812/N+Vdaa7hSmO3ubYMVFAxxyDVT8osWVliZEdLle894TawR50IjGuD2hsA0QvRo45rqoSvZf9+1XPqboe+OIgUJsClPvWomtuFj2hgwfVaa3HA8ybB3yaIk5j3W61WrecdahpgReG5H3jKIGyMkA31ECORS3kRGf8m8WbTHfIfXK7RSqjtVUdC15NnPm4XP7iWIWrLkbd2io4juajeOp+sd3u3aLOyOHD6njcv1+cxVVXqzKnmha4co9cck2+vkyPcHp5pzFAJ5pQB69xeSNABGh5sMsCPZYmcUpr/lqNrLAcBcaPEafe8rTWXAFcdBGQsQM4/2zxYZUpEVnMPztbPb/NA6RbAZsLsKeKNjkRwlymaiCT+pvF+3TeZlOTV+Q+DRkijovRo9WxkLETGH2Wob43UIcWlHw6AAVDKlQO+ptv1PNkGdagzMoSAbqjWtKyA+F2s850FzBA9wYffKDqApvviO++EFGnMUDHm7FHYayHIHvLbre4X12teiPl5WKs6pdfqtPLxiZxKtrSLFIXAFBVC0yeLJ738cdF248bgP/8B9DSxVhXjyewjnGb4ke6Ol02jhI4dkxMzpBX/+XECKD3nsLGsnd4vNcKrhGtaSKV5fGoSSmaJnrHbrc4JgCgxSLSYk26GgHyySfiIuOGDeqi35Yt4uztq6/UxdHGRjW6yGoVPWq5faip4x3VniEADNCJIbh+M6BOKeWpIRB4epmRIQ5weRpq/lpcpbduB8b4lqsyfQCsXw/MnSseA4ATdgEXnAmcUCY+YGedpepuyKI7htrCjr5eFFxjgSPbC9gsYruBA8WH0mxWV//l79GbT2FjVW70eM8famms004TqazaWsOklCqR2sjMBB57TLTVzwEeXQo0zgVOmyDarFbglFNQ+OI01JWKVIij2oLi2Xkih+3rTBTeZEVdqxmOjBQUr/YdK8cruNTbj5njYIBOdh3lrGFSM83a+9ngGs9BbcUv+AKzsxWAJWK7TTFgfC87OgYuvhj485/F90O2A++8IwphBW1X12LzL+xecF5Km6eqazCjZK2vYBdFBAN0vAUvW6Vp6hZQk1GcTpV2kPflF6DqNuswjLbQ1c/LOhH+Ws96+BezjBNpNE0M1bviCtG2Z094z0WxJ9/v4FFCuiGNpeu+muBQx1Frq+8xzfAcvoJZsjQpIGq5yFFIni4cX9QGA3QkHKdWc4eMg//lhIDycpXiqKoSqY2yMpXi2LhRpBZ271bryzWZRI7QeYLKKcsazi6Xv/iRw9KEgstb4UjzrVVotQYW+TEG4Y5+p/p6INMBPPCA+F5ehOzNErFEpxxmJ4trmc3+wOuwu1EwtRHYexOwTcwSdWi1YmSP1wt89JH42a+/FsdfXZM63ur7ANVpYm1LOSqkMQtAGuDxAk6PWtcSEEP0AHF8yZy1LPqlaYaaMmTEAB0pHdRqPi45TMlqFblls1nlCSsr1bApf77ZLPLAqamq8E3DeuB/nxQ5xLmP+p5vrliTbvBg/1C64utXAQ+PAz48IuoD5+Wp4j7ylFi+nvH3MZ4uZ2eLn0lPA8YbctC9XaIVuE9PV//8Bw4U75thPHLxokrgkpHA/W8DCxeK7eY9Dpy/WBRSuuQS0bZ6tchVW3eq4838tboOIWcrmqrUslrymJLXN2TuO3g6vN2eWOPHEwwDdE+haWJW2Mnb4E8UnrwtvvtEScykzop27YrvrvRiDNCRZixqFFyHwJjOcDpF70HW3wXEqaXdLk4tZa/6ww9FamPbNv+kkMKVP0bd2mo4vrkMxZPfEtt504CNG+HwNKJgvC+dodWI4VNHjhiKJTWpU0+Z2jCmM0KdARjXugtukzU4nE41C7G3Ln2V6KMRgodLyqF3LS3qffzhB2DtWjEpaeZM0TZjhuhF1w4G3n4bAOBozkTBBf2BA9OB/7SINmcVUKkBP2jAzmox/l5Ogtq9W/Tcy8vV8VFfr1Jsxs+KPBNhcSUG6KgIVSdXHmwy8MoADaiZf3JKrc2mhq9t2CBOT+12/1C5Oq0FJZ9koyDNArz4otguZS4wfjyKf3478OefiLai/wMuWCTGRctiSWVl4nRVpims1tBFmIK/N7bLkSFpacDoH4k2k0l8AHvz0lfxXNX7eGw28ZWRoQKgHHp33nmqEuE554jURtZ2oGCKaDvhBOD664F7PvevAF/8zi3A888DNxapY/DxUiBnPmCpFHnpt95S4/SzssTyWuXl6riX+2W8ZZ3pAAzQyUymNQCmMyh6rFaR7jg6R6U9WHslJhigOyv49F5efZYrJxtHYmRmih6AnA0oHy8vV7Uzdu8WF/0+/FD1Hv7v/0Qv9F//UlfGt20Tvd69e9VV9XodeP11ODQvCiaKWWEOb43obVdUiFsAaGgQr9PcrGaFAeoUN9Swq85yOuFIdaHgIt/V/8ZnUFxbK3rQvTXFkYi9PuM+yWFzxve7ulpsY6wbffAgHNYmFFwoLug5mp9F8UwP8J8TgHvvFdtddRUwbpw4vs4/X7Rt2CB630ctwJZycdzK1Nr69aIHbawzvWGD6Lnv3i3qwwBitNEpp4jPkRy15PGoY6qXpT1612/bXcaRDcYADQSetqWnq4L3xjZAjc6QIzHMZlVwyG4HLrgAePppYOpU0fbGG2q2lzyIXWuBl19G8UnbgE+uE213lABTnhT5wylTRNu+fSIt8pOfqCvtMscnr/AHT1bpDN/vWvxULTBR/CMpsDrEB6o3pzgSLXgY90cGOOMx27+/OK7GjFHHxwUXAKecguK7SoDbLwAAFJycCxScA5zwBTDelwq57DLghRfEKI3bbhNt//mPCLimXeI5s7JUam3tWpFOsdlU2kMe+998o67DVFWJfa2tVftcW9tr0x4JdkRRu9atM4w1vl2kNqZPj+suEfnTHwCwY0d896UHYoAOlbqorla9jOZm0es1rqAtT7+cTiArC4XXe1AnB/97vCh+8aDI0cn6F7t3i/sy9QAAr78unvP999VY0c2bxYWVf/xDnTZ++y1wwQUo/G4B6vqJXrUjbaO4MFNbKwrZAOpKvMulVm12u8VreDxqlW6gbU+kO6MPZC1pTROns+WGhWQpMQUX6NK0wAJK8ixIpic0TaTf3NniFhAjgz74AMjNVT3oX/1KTBmvHilGfWzZoq6RbN4sfnb9etG7BkSv2mYTaT6Z0vvoI9Gr/+QT1fvev198HiorA+vRyM9ND8YADbRNXQAqFdHcrFIWxgCdkeEfNlfXYEHJP8Vpf8EEG3DKieK0Teba9u0T9w0jMZCSIvJ1L7wAXHutaHvgARF4MzNVRbrcXGDGDNTNXoeSf/tOA29+C7hxpchLT5wo2g4eFKeXtbVqMkFWlniuIUNU7ttm63jURjiGDTOcru4WH6jy8q4/H0Wf8f2Waa6cHHW8y4lLF12kRhKdeAC4dAyQ9Q1wqa/j8K9/idTZq68Cl14q2jIzgdtvB+Z/AKxZI47Jv/xFPPbNN2K7VatU5+P114Grrwa2bvXXo8aHH4p9sNlU2mX/fnEsHzumgrKxAmMPloDjgXqZTz8Vp4jyNLGkRFx8kVJSxGMmvlWU4Gy2wOP4oovErb/CUoE43qnTekcP2jhqQY6YMA6Sl6MtKitF7+HDD9WFuw8/BC65BIWz7Kiz1QEAHPW1KH5yn+jlXn45cAjAu75T/f0nASvfAd59V502vvIKUF6Owt8PRN3ib8VzlE9E8YmLgaNHRU8EENv/5S8oXH8D6s7z9UTrVgH7B8Nxwh5gxQrRVlsr0hgej6rHLJc0AgLTGfJ01bhcVSQvtvh+RwdqUXDmt3A4+6OYZTmShyxuJI9VQBwzmuY/nhypLhRMrIbD2qzSHs3NIvA2NgKlpaJtyBDgpz+F4z9uFJz9HVD+G2D0QfHYDyuBg3nAwR3A6aKwluOHaSges0Z8jlauFNu9+aY423vrLVXG9HYH6nLr4DhWj+JHxUQZVFYCv/lNNP8yiUFPIlOnTtWnTp0a/g+6XOK2qUm1/fCDuK2oUG3ffSduX31VtT31lK7ruj597GF/0/SR+wMf+9FX6rFBW8Wdm25Sz3HtteKx3M1qu7S1uj59uq4PG6a2+8MfxGPDdqq2uXPF7b33qraXXhK3m9Xz6YcOidu6OtUmf295q+u63tqqR0xLi7o/cKCu67o+PW9b5J4/iUzH3+O9C+HzesWt8ZgI9Vk5ckTc7tmj2jZtErdvvqnaHnhA3PqOY/2ee9Rj998vbk85RRz306erz8Bpp6ntrrtO3N52m79p+tAvxe1pu9V2zzxzvN/Or8txIwGEfd6saRqWLl2KSZMmYdSoUbj55ptx4MCBdrevqanB7373O4wdOxZjx47FAw88gObeWhyltqZtOmPSpPjuU6TIVEz1D/HeE0pk55+v0h4nDhC3iVgFMEGEHaCXLVuGNWvWYOHChXjttddgMpkwa9YsuNv5I8+dOxeHDh3Ciy++iKVLl+Lf//43HnnkkW7veEKprFSB94dj4lauA1hQIK5MG4NySQmQ1Te++xxp48eL30vT470nlGxk7tr4WSktVW1VR+O9h3ETVg7a7XZj5cqVKCoqwuTJkwEAS5YswaRJk7Bu3TpcIYu3+3z++ef47LPPUFpaiuHDhwMAFixYgJkzZ+Luu+/GSSedFKFf4zjeekscBGVl4qoxIK4un3oqCv/7R6iz+3LLrioUP7YRhTeYUHfLerFd63DgmTI4ju0DFjwn2r4eC5x2MQoP/Dfq/rwHjuZKYOhQ8ZjXC5xxhrgyPXiwaPv4YzE7StOABx8Ur9V0HgpOTQfqfwOM8dXKPfpL4IMKOCwNYggSIGYDrl4tcn4y3+zxiKF7xkL8gMozhyp+FK1B/sbn/Z//AVpHca25ZNJRISxjp0tOvJLlQjVNjNLweAILZq1aJbb98MPA/PThw+IayqFD/tVbHO58FJxbDhx9HrD4RmdUfwfszgOOHgZ2iTaHc7s4tvadA5zmG/vf1MQcdLDt27frp512mr5v376A9muvvVZ/6KGH2my/YsUK/cILLwxoc7lcen5+vv7ee++FnY/pci5J5pR9OWNd1/05semnfOFvmj7El+uy/UNtN2yYyJOdfHJg7kzX9elp60TbkCHqOU725WDvuEM9h+9+QG75iSfE7aefqraPPxa3W7eqNpnr27VLtdXUiFtjDljel3nFWDDmLn25yOmWd1VbLPclzpIyBx2KfM+M712oXLU83hoaVJvMVe/dK26Nx/G//iVu//hH1bZkibidN0+1zZolbu+6S7UNHiw+d+nrVNuAAcf9VaRkzkGbdF3v9Dnp2rVrMWfOHGzfvh12w9TmO+64A06nE88++2zA9gsXLsT27dvx17/+NaB9woQJmDlzJm655ZY2rzFt2rR2X7+8vBwWy/9r7/5jmjrXOIB/O7QdMJjDi8WMy503Ws2sA7vbyoTodMAfhGzuVwYO54DgshjIRiYbg6iE/YgrY4TpzDTGxS0dyTJMJrlbNpYt0U1Gccm8BFvwBwNGoQIDhkgr8Nw/yimn9KDUIT3W55MQ6ul78H37nvNwOD+eJwhLly6dbZddurqAoCBX6Z6Fk7XUnE5AqUTP1XCog11HB8LrnpFwqJcFu9pdvgxERk59B9DT7oA6RuX+DrvdfZ9xT7sT6hilK23j4sWunzH5uqfjGtT/nPz/BweBe+91PViiUrmWCRnuJvsGYOp9cd+F+7WJXFnkAM/X/nDlChAaip5LI1Avu/OqY/RcHIH633fQuIXtTbzdjY977mfi7VjYtsU5NoR9oL9/6j7s3l7XY9/i/Wdy/3LvWwDw++9Tf6HegM1mQ1BQEP53Gz7p6NMpjqtXXXlfldMqR6hUKgwODkq2n95WaO9wOHz5rwEACoUCC3zMd2Cz2QCFAktnOJ3iWhrs8dqj5WRQdn8HXEFZ9N39EAgwtQEJG5fotTs4A64NE5gKzsBUPg/xZya8v1C0rnDaQByQpwVnm80GAL7/MrtZoaEA4NfgPO9jFvFncPbLuIXtTbzdBU0WFRa2VfF2LGzb4icBhX1ACM7AVE4O8f4zuX+59y0A+Ne/Zj3uBQsWSMah24FP0U44anY6nR5H0A6HA8HBwZLtpS4eOhwOhMxQg+z777/3pUs3JByRz/XPlbs7cdx34pgBHncgj9unKzjCbyq7+EGIyX9HSTwXHxUV5dXW6XRiYGBg/i4QMsbYbcqnAL1q1Srcc889+OWXX9zLhoaG0NzcjP8ICVBE9Ho9uru7Pe6TFtbVCYlQGGOMSfLpFIdSqURmZibKy8sRERGB+++/H0ajEVFRUUhOTsb4+Dj6+/sRFhaGu+++G7GxsdDpdHj11Vexd+9ejIyMYM+ePdiyZQsfQTPG2A34fJNqfn4+nnnmGZSUlCAjIwNBQUE4cuQIlEolbDYbEhMT8d/Jex8VCgX279+P6OhobN++Ha+88go2bNiAvXv3zvU4GGMs4PicLCkoKAi7du3Crl27vN6Ljo6G1Wr1WLZ48WJUVVXdfA8ZY+wOxY95McaYTPn0oApjjLH5w0fQjDEmUxygGWNMpjhAM8aYTHGAZowxmQq4AN3Q0ICVK1d6ff38888zrtPZ2YmXXnoJOp0O69evh9FoxPj4+Dz2+u+x2WwoKChAQkIC9Ho9cnJy0Nraet119u/fL/k5jd2qvNFz4E6t5jMwMIDdu3djw4YN0Ol0yMjIQGNj44ztjx8/Ljm31/us5OiPP/6QHMf07JiCQJlvsYArGmu1WhETEwOTyeSx/F4hc9Y0165dQ05ODpYtW4bq6mq0t7ejuLgYKpUK+fn589Hlv8XpdGLHjh2IiIjAxx9/DJVKhQMHDmD79u2ora1FhDhTmIjVasUTTzzhdT+7r9kC55NQzefdd9+FWq2G0WhEbm4uamtrJbOV5efnw+Fw4JNPPsHQ0BCKi4tRWlqKffv2+aH3N6+goAB9fX2oqKhAREQETCYTcnJyUFNT4y6EIWa1WmEwGFBRUeGxfKZtQa6sVitUKhXq6uqgEGXNCwsLk2wfKPPtwb/pqOdeSUkJvfzyy7Nuf+LECdJqtTQoKrZaXV1NOp2OHOJiqzL1008/kUajoe7ubvcyh8NBsbGx9MUXX8y4XkpKCh09enQeejg3HA4HrV27lkwmk3vZ4OAgPfTQQ1RbW+vV/tdffyWNRkPnz593Lzt58iStXLnS47OSu7a2NtJoNHRGlPx+YmKCkpOTqbKyUnKdrKwseuutt+ari7fMwYMH6fHHH59V20CZ7+kC7hSH1WrF8uXLZ92+sbERq1evRvhkiXcAiI+Px/DwMCwWy63o4pxasWIFDh065JXbhIgkc3QDrjzd7e3tPn1O/maxWHDlyhXEx8e7l4WHh+PBBx+E2Wz2at/Y2IjIyEiPI0yDwQCFQoEzZ87MS5/nwn333YdDhw5Bq9W6lykUiuvOr6/7gFz5Mo5Ame/pAipAExFaW1tx4cIFPPXUU0hISEBWVhbOCrX8JHR3d3ulSl0ymSC8q6vrlvZ3LkRGRrrrQwqOHTsGh8OBhIQEyXVaW1sxMTGBb775BikpKXj00UdRWFjolRpWTrq7uwF4J2dfsmSJO3G7WE9Pj1dbpVKJRYsWSbaXq/DwcGzcuNHjFM7XX3+N9vZ2JCYmerXv7+9Hb28vzGYz0tLSkJiYiJ07d+LSpUvz2e050dLSgr6+PmzduhXr169HRkYGTp48Kdk2UOZ7OvmecJTQ2dl53ZJY1dXVGBkZgdPpxO7du6FQKHDs2DFkZmaipqZG8rfx6Oiox9Ez4Kr4AuCmqr7MtRuN+dSpU4gUVXv59ttv8cEHH2Dbtm1YtWqV5DrCBcSwsDBUVVWht7cXFRUVeOGFF3D8+HHJ4gv+5u9qPnJx5swZvPnmm3jsscewefNmr/dbWloAuHLm7Nu3DyMjI/joo4+wdetWnDhxAv8QKpbInNPpRFtbG4KDg1FYWIiQkBB89dVXyM3NxdGjR/HII494tA/U+b6tArRarXZnypPywAMPoLGxESEhIQiaLL9jNBqRlpaGTz/9FKWlpV7rSFV9ESZ0pqov8+lGYxZf+Pn8889RVlaG1NRUFBUVzbjO008/jaSkJI8LpytWrMDGjRvxww8/IDU1dW46P4fmo5qP3NXV1eG1115DbGys1wVAQXx8PBoaGjzm9sCBA9i0aRNqamqwY8eO+eru36JUKmE2mz3KVWm1Wly4cAFHjhzxCtCBON/AbRagFy5cKHnVWmz6Fd677roLy5cvR09Pj2T7qKgo91GHQPhTXw45q2czZgAoLy/H4cOHsW3bNhQXF3tc9ZYy/a4WtVqNRYsWuU8lyI24mk9MTIx7ud1ul/xLISoqCnV1dR7LbudqPp999hnefvttJCcno7y8/Lo19qbPbUhICKKjo2fcB+RKKrBqNBqcOnXKa3mgzbcgoM5B//jjj4iLi/M45zQ2NgaLxTLjxQa9Xo/m5mYMDw+7l50+fRqhoaEzniKQG6PRiMOHD6OwsBAlJSU3DM7vv/8+UlNTQaI8WZ2dnfjzzz9le3HpTq7mYzKZUFZWhueffx6VlZXXDc4mkwnr1q3D6Oioe9nw8DDa2tpkO7dSLBYL1q5d63W/d1NTk+Q4Amm+Pfj3JpK59ddff9HmzZspMzOTmpqayGKxUEFBAen1erp8+TIRuW7Xstvt7lvoRkdHKSkpiXJycujcuXP03XffkcFgoA8//NCfQ5m1+vp60mg0VFZWRna73eNreHiYiLzH3NTURFqtlkpLS+nixYvU0NBAW7ZsofT0dJqYmPDncK6roqKCDAYD1dXV0blz5yg7O5tSUlLI4XDQ2NgY2e12unr1KhG5bkVLT0+nJ598kn777Tc6ffo0bdq0id544w0/j8I3Fy9epNWrV9POnTu95ndoaMhr3F1dXaTX6ykvL49aWlro7Nmz9OKLL1JSUpK7ze1gfHycnn32WUpLSyOz2Uznz5+nd955h7RaLVksloCd7+kCKkATEbW3t1NeXh4ZDAaKjY2l7Oxsslqt7veFgFZfX+9e1tbWRllZWbRmzRpKTEykyspKGh8f90f3fVZSUkIajUbyq6qqioikx1xfX0/p6ekUFxdHBoOBioqKaGBgwF/DmJWxsTF67733KD4+nuLi4ig3N5c6OjqIiKijo4M0Gg19+eWX7va9vb2Ul5dHcXFxtG7dOtqzZw+Njo76q/s35eDBgzPO7+uvvy457ubmZsrOzqaHH36YdDod5eXlUVdXlx9HcXP6+vqoqKiIEhISaM2aNfTcc8+R2WwmosCd7+k4HzRjjMlUQJ2DZoyxQMIBmjHGZIoDNGOMyRQHaMYYkykO0IwxJlMcoBljTKY4QDPGmExxgGaMMZniAM0YYzLFAZoxxmSKAzRjjMkUB2jGGJOp/wPZYWFcQEmPrAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Posterior Predictive\n",
    "hddm.plotting.plot_posterior_predictive(\n",
    "    model=hddm_models_link_id[0],\n",
    "    columns=2,\n",
    "    figsize=(8, 6),\n",
    "    value_range=np.arange(-5, 5, 0.05),\n",
    "    parameter_recovery_mode=False,\n",
    "    required_method=\"random\",\n",
    "    plot_func=hddm.plotting._plot_func_posterior_node_from_sim,\n",
    "    **{\n",
    "        \"alpha\": 0.01,\n",
    "        \"ylim\": 3,\n",
    "        \"bin_size\": 0.1,\n",
    "        \"add_posterior_mean_rts\": True,\n",
    "        \"add_posterior_uncertainty_rts\": True,\n",
    "        \"plot_likelihood_raw\": False,\n",
    "        \"samples\": 200,\n",
    "        \"legend_fontsize\": 7,\n",
    "        \"subplots_adjust\": {\"top\": 0.9, \"hspace\": 0.3, \"wspace\": 0.3},\n",
    "        \"posterior_uncertainty_color\": \"red\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad42048-2bfa-4a4b-87de-4eeaa22923e0",
   "metadata": {},
   "source": [
    "##### NOTES:\n",
    "\n",
    "If we *do not* use the *identity* link on a parameter, the *prior* on it's *intercept* will default to a *normal distribution*.\n",
    "\n",
    "If we *do* use the *identity* link on a parameter, the *prior* will be the same as the *informative* prior chosen when estimating this parameter *without a regression model*."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b5ab483-9316-4e3d-a375-4e550256fc94",
   "metadata": {},
   "source": [
    "#### Example 3: Less Standard use case - Stimulus Coding "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2963d7f1-d26e-4550-8219-2bbf5a7accee",
   "metadata": {},
   "source": [
    "We will refer to the slides to discuss stimulus coding, before proceeding to the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c1b42d67-4207-4a72-8cf2-045a119b556f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate some StimCoding Data\n",
    "n_samples = 1000\n",
    "v = np.concatenate([np.ones(500), -np.ones(500)])\n",
    "a = 3\n",
    "z = 0.5\n",
    "t = 1\n",
    "\n",
    "theta = np.zeros((n_samples, 4))\n",
    "theta[:, 0] = v\n",
    "theta[:, 1] = a\n",
    "theta[:, 2] = z\n",
    "theta[:, 3] = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "72280c2f-80d6-4b4d-90ac-589c8c3ce35e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_out = hddm.simulators.simulator(\n",
    "    theta=theta, model=\"ddm_hddm_base\", n_samples=1, max_t=40\n",
    ")\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.hstack([sim_out[0], sim_out[1], theta]),\n",
    "    columns=[\"rt\", \"response\", \"v\", \"a\", \"z\", \"t\"],\n",
    ")\n",
    "data[\"stim\"] = 2\n",
    "data.loc[data[\"v\"] == -1.0, \"stim\"] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a469be09-e1d8-48ec-81ba-4581bab923c8",
   "metadata": {},
   "source": [
    "##### VIA STIMCODING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c83de630-c7c7-4186-b747-14af48277fb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 1500 of 1500 complete in 24.1 sec----------      31%                  ] 470 of 1500 complete in 7.6 sec"
     ]
    }
   ],
   "source": [
    "hddm_models_stim = []\n",
    "n_models = 1\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_stim.append(\n",
    "        hddm.HDDMStimCoding(\n",
    "            data,\n",
    "            split_param=\"v\",\n",
    "            stim_col=\"stim\",\n",
    "            informative=True,\n",
    "            is_group_model=False,\n",
    "            include=[\"v\", \"a\", \"t\", \"z\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 1500\n",
    "for i in range(1):\n",
    "    print(i)\n",
    "    hddm_models_stim[i].sample(nmcmc, burn=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "36ec76ed-5fb8-4b41-9333-dc5af299fb7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.050481</td>\n",
       "      <td>0.07143</td>\n",
       "      <td>2.919683</td>\n",
       "      <td>2.99879</td>\n",
       "      <td>3.0508</td>\n",
       "      <td>3.097084</td>\n",
       "      <td>3.193658</td>\n",
       "      <td>0.003983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>1.047365</td>\n",
       "      <td>0.030412</td>\n",
       "      <td>0.989256</td>\n",
       "      <td>1.02644</td>\n",
       "      <td>1.046312</td>\n",
       "      <td>1.069873</td>\n",
       "      <td>1.102508</td>\n",
       "      <td>0.00137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.993757</td>\n",
       "      <td>0.016443</td>\n",
       "      <td>0.960173</td>\n",
       "      <td>0.982792</td>\n",
       "      <td>0.993751</td>\n",
       "      <td>1.005586</td>\n",
       "      <td>1.023616</td>\n",
       "      <td>0.000839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.501646</td>\n",
       "      <td>0.008078</td>\n",
       "      <td>0.486367</td>\n",
       "      <td>0.496445</td>\n",
       "      <td>0.50116</td>\n",
       "      <td>0.507025</td>\n",
       "      <td>0.518637</td>\n",
       "      <td>0.00028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean       std      2.5q       25q       50q       75q     97.5q  \\\n",
       "a  3.050481   0.07143  2.919683   2.99879    3.0508  3.097084  3.193658   \n",
       "v  1.047365  0.030412  0.989256   1.02644  1.046312  1.069873  1.102508   \n",
       "t  0.993757  0.016443  0.960173  0.982792  0.993751  1.005586  1.023616   \n",
       "z  0.501646  0.008078  0.486367  0.496445   0.50116  0.507025  0.518637   \n",
       "\n",
       "     mc err  \n",
       "a  0.003983  \n",
       "v   0.00137  \n",
       "t  0.000839  \n",
       "z   0.00028  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_stim[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca9a086-6513-44b4-9201-e2f82720c1d0",
   "metadata": {},
   "source": [
    "##### VIA REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2897879c-e72d-4de2-af95-49f356065d47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 114.0 sec"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrix\n",
    "\n",
    "\n",
    "def identity_link(x):\n",
    "    return x\n",
    "\n",
    "\n",
    "def logit_link(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "def stim_coding(x, data=data):\n",
    "    stim = np.squeeze(\n",
    "        (np.asarray(dmatrix(\"0 + C(s,[[-1],[1]])\", {\"s\": data.stim.loc[x.index]})))\n",
    "    )\n",
    "    return x * stim\n",
    "\n",
    "\n",
    "reg_models = [\n",
    "    {\"model\": \"z ~ 1\", \"link_func\": identity_link},\n",
    "    {\"model\": \"v ~ 1\", \"link_func\": stim_coding},\n",
    "]\n",
    "\n",
    "hddm_models_stim_reg = []\n",
    "n_models = 1\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_stim_reg.append(\n",
    "        hddm.HDDMRegressor(\n",
    "            data,\n",
    "            reg_models,\n",
    "            informative=True,\n",
    "            is_group_model=False,\n",
    "            group_only_regressors=True,\n",
    "            include=[\"v\", \"a\", \"t\", \"z\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 2000\n",
    "for i in range(1):\n",
    "    print(i)\n",
    "    hddm_models_stim_reg[i].sample(nmcmc, burn=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "68f54c12-6401-48f3-9ca7-1791de0d6b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.054358</td>\n",
       "      <td>0.07563</td>\n",
       "      <td>2.911396</td>\n",
       "      <td>3.000785</td>\n",
       "      <td>3.054716</td>\n",
       "      <td>3.106933</td>\n",
       "      <td>3.203144</td>\n",
       "      <td>0.004325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.993407</td>\n",
       "      <td>0.017252</td>\n",
       "      <td>0.956947</td>\n",
       "      <td>0.98271</td>\n",
       "      <td>0.994297</td>\n",
       "      <td>1.005285</td>\n",
       "      <td>1.025845</td>\n",
       "      <td>0.000923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_Intercept</th>\n",
       "      <td>0.502034</td>\n",
       "      <td>0.008245</td>\n",
       "      <td>0.48624</td>\n",
       "      <td>0.496491</td>\n",
       "      <td>0.502031</td>\n",
       "      <td>0.507322</td>\n",
       "      <td>0.51889</td>\n",
       "      <td>0.000273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept</th>\n",
       "      <td>1.049736</td>\n",
       "      <td>0.032456</td>\n",
       "      <td>0.986095</td>\n",
       "      <td>1.028932</td>\n",
       "      <td>1.050385</td>\n",
       "      <td>1.071251</td>\n",
       "      <td>1.110863</td>\n",
       "      <td>0.00133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean       std      2.5q       25q       50q       75q  \\\n",
       "a            3.054358   0.07563  2.911396  3.000785  3.054716  3.106933   \n",
       "t            0.993407  0.017252  0.956947   0.98271  0.994297  1.005285   \n",
       "z_Intercept  0.502034  0.008245   0.48624  0.496491  0.502031  0.507322   \n",
       "v_Intercept  1.049736  0.032456  0.986095  1.028932  1.050385  1.071251   \n",
       "\n",
       "                97.5q    mc err  \n",
       "a            3.203144  0.004325  \n",
       "t            1.025845  0.000923  \n",
       "z_Intercept   0.51889  0.000273  \n",
       "v_Intercept  1.110863   0.00133  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_stim_reg[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ff1181-cb6f-4fb0-bc7a-fb3ecead1799",
   "metadata": {},
   "source": [
    "##### VIA REGRESSION - ALTERNATIVE VERSION (VIA COLUMN TRANSFORM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4db9bd70-157a-4912-808e-25b0a40e00dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"stimalt\"] = 1\n",
    "data.loc[data[\"stim\"] == 2, \"stimalt\"] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7492b511-2990-4737-a927-06579a4e016d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>v</th>\n",
       "      <th>a</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "      <th>stim</th>\n",
       "      <th>stimalt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.250002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.806993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.374008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.006991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.939991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.500014</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1.833993</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1.875992</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.031992</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.596019</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           rt  response    v    a    z    t  stim  stimalt\n",
       "0    2.250002       1.0  1.0  3.0  0.5  1.0     2       -1\n",
       "1    1.806993       1.0  1.0  3.0  0.5  1.0     2       -1\n",
       "2    2.374008       1.0  1.0  3.0  0.5  1.0     2       -1\n",
       "3    2.006991       1.0  1.0  3.0  0.5  1.0     2       -1\n",
       "4    1.939991       1.0  1.0  3.0  0.5  1.0     2       -1\n",
       "..        ...       ...  ...  ...  ...  ...   ...      ...\n",
       "995  2.500014      -1.0 -1.0  3.0  0.5  1.0     1        1\n",
       "996  1.833993      -1.0 -1.0  3.0  0.5  1.0     1        1\n",
       "997  1.875992      -1.0 -1.0  3.0  0.5  1.0     1        1\n",
       "998  2.031992      -1.0 -1.0  3.0  0.5  1.0     1        1\n",
       "999  2.596019      -1.0 -1.0  3.0  0.5  1.0     1        1\n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "4f7e4b39-8907-45c4-b66c-904ace602c4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 34.5 sec"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrix\n",
    "\n",
    "\n",
    "def identity_link(x):\n",
    "    return x\n",
    "\n",
    "\n",
    "def logit_link(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "def stim_coding(x, data=data):\n",
    "    stim = np.squeeze(\n",
    "        (np.asarray(dmatrix(\"0 + C(s,[[-1],[1]])\", {\"s\": data.stim.loc[x.index]})))\n",
    "    )\n",
    "    return x * stim\n",
    "\n",
    "\n",
    "def stim_coding_alt(x, data=data):\n",
    "    return x * data.stimalt.loc[x.index].values\n",
    "\n",
    "\n",
    "reg_models = [\n",
    "    {\"model\": \"z ~ 1\", \"link_func\": identity_link},\n",
    "    {\"model\": \"v ~ 0 + stimalt\", \"link_func\": stim_coding_alt},\n",
    "]\n",
    "\n",
    "hddm_models_stim_reg_alt = []\n",
    "n_models = 1\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_stim_reg_alt.append(\n",
    "        hddm.HDDMRegressor(\n",
    "            data,\n",
    "            reg_models,\n",
    "            informative=True,\n",
    "            is_group_model=False,\n",
    "            group_only_regressors=True,\n",
    "            include=[\"v\", \"a\", \"t\", \"z\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 1000\n",
    "for i in range(1):\n",
    "    print(i)\n",
    "    hddm_models_stim_reg_alt[i].sample(nmcmc, burn=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "ac6d53f5-9e4c-46a5-baf2-575e230fe6ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2.275273</td>\n",
       "      <td>0.033777</td>\n",
       "      <td>2.20881</td>\n",
       "      <td>2.251518</td>\n",
       "      <td>2.275784</td>\n",
       "      <td>2.300361</td>\n",
       "      <td>2.34066</td>\n",
       "      <td>0.001857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>1.096356</td>\n",
       "      <td>0.010503</td>\n",
       "      <td>1.07462</td>\n",
       "      <td>1.089121</td>\n",
       "      <td>1.097411</td>\n",
       "      <td>1.104284</td>\n",
       "      <td>1.115353</td>\n",
       "      <td>0.000571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_Intercept</th>\n",
       "      <td>0.492462</td>\n",
       "      <td>0.010664</td>\n",
       "      <td>0.472059</td>\n",
       "      <td>0.485721</td>\n",
       "      <td>0.491887</td>\n",
       "      <td>0.499742</td>\n",
       "      <td>0.513756</td>\n",
       "      <td>0.000633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_stimalt</th>\n",
       "      <td>0.018102</td>\n",
       "      <td>0.034652</td>\n",
       "      <td>-0.054564</td>\n",
       "      <td>-0.003809</td>\n",
       "      <td>0.020024</td>\n",
       "      <td>0.039782</td>\n",
       "      <td>0.084198</td>\n",
       "      <td>0.001998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean       std      2.5q       25q       50q       75q  \\\n",
       "a            2.275273  0.033777   2.20881  2.251518  2.275784  2.300361   \n",
       "t            1.096356  0.010503   1.07462  1.089121  1.097411  1.104284   \n",
       "z_Intercept  0.492462  0.010664  0.472059  0.485721  0.491887  0.499742   \n",
       "v_stimalt    0.018102  0.034652 -0.054564 -0.003809  0.020024  0.039782   \n",
       "\n",
       "                97.5q    mc err  \n",
       "a             2.34066  0.001857  \n",
       "t            1.115353  0.000571  \n",
       "z_Intercept  0.513756  0.000633  \n",
       "v_stimalt    0.084198  0.001998  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_stim_reg_alt[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0faf47-35cf-4c21-a5d1-b795e9b1b32c",
   "metadata": {},
   "source": [
    "### Interactions (Continuous Covariates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "d9aac268-4d64-4eef-9fae-0229806aeb60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make data\n",
    "n_trials = 1000\n",
    "\n",
    "beta_v_intercept = 1\n",
    "beta_v_cov_1 = 1\n",
    "beta_v_cov_2 = -1\n",
    "beta_v_interaction = 0.5\n",
    "cov_1 = np.random.uniform(low=-1, high=1, size=n_trials)\n",
    "cov_2 = np.random.uniform(low=-1, high=1, size=n_trials)\n",
    "\n",
    "v = (\n",
    "    beta_v_intercept\n",
    "    + beta_v_cov_1 * cov_1\n",
    "    + beta_v_cov_2 * cov_2\n",
    "    + beta_v_interaction * cov_1 * cov_2\n",
    ")\n",
    "a = 3\n",
    "t = 1\n",
    "z = 0.4\n",
    "\n",
    "theta = np.zeros((n_trials, 4))\n",
    "theta[:, 0] = v\n",
    "theta[:, 1] = a\n",
    "theta[:, 2] = z\n",
    "theta[:, 3] = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "1845326e-d319-47dc-9640-3d5a61acb1b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_out = hddm.simulators.simulator(\n",
    "    theta=theta, model=\"ddm_hddm_base\", n_samples=1, max_t=40\n",
    ")\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.hstack([sim_out[0], sim_out[1], theta]),\n",
    "    columns=[\"rt\", \"response\", \"v\", \"a\", \"z\", \"t\"],\n",
    ")\n",
    "data[\"cov_1\"] = cov_1\n",
    "data[\"cov_2\"] = cov_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "2054b8f9-ccd6-4427-a9c0-46a69b7e5888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>v</th>\n",
       "      <th>a</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "      <th>cov_1</th>\n",
       "      <th>cov_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.177025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.868726</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.539517</td>\n",
       "      <td>-0.321512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.721994</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.842933</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.435072</td>\n",
       "      <td>0.756762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.522997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.273635</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.698202</td>\n",
       "      <td>-0.884059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.206000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.455309</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.333299</td>\n",
       "      <td>-0.675959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.682995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.754868</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.146508</td>\n",
       "      <td>-0.839854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1.622996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.205304</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.278095</td>\n",
       "      <td>0.814022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.473013</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.621518</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.101141</td>\n",
       "      <td>0.263991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1.402998</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.659651</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.993290</td>\n",
       "      <td>0.662830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.552016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.749547</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.983792</td>\n",
       "      <td>0.461018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.919034</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.346598</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.138464</td>\n",
       "      <td>0.481596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           rt  response         v    a    z    t     cov_1     cov_2\n",
       "0    3.177025       1.0  0.868726  3.0  0.4  1.0 -0.539517 -0.321512\n",
       "1    1.721994       1.0  0.842933  3.0  0.4  1.0  0.435072  0.756762\n",
       "2    1.522997       1.0  2.273635  3.0  0.4  1.0  0.698202 -0.884059\n",
       "3    2.206000       1.0  1.455309  3.0  0.4  1.0 -0.333299 -0.675959\n",
       "4    1.682995       1.0  1.754868  3.0  0.4  1.0 -0.146508 -0.839854\n",
       "..        ...       ...       ...  ...  ...  ...       ...       ...\n",
       "995  1.622996       0.0 -0.205304  3.0  0.4  1.0 -0.278095  0.814022\n",
       "996  2.473013       1.0  0.621518  3.0  0.4  1.0 -0.101141  0.263991\n",
       "997  1.402998       1.0  1.659651  3.0  0.4  1.0  0.993290  0.662830\n",
       "998  2.552016       1.0  1.749547  3.0  0.4  1.0  0.983792  0.461018\n",
       "999  2.919034       1.0  0.346598  3.0  0.4  1.0 -0.138464  0.481596\n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1d608a01-f2d1-4b7b-aebc-b140b97ead9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 128.2 sec"
     ]
    }
   ],
   "source": [
    "# Run Model\n",
    "def identity_link(x):\n",
    "    return x\n",
    "\n",
    "\n",
    "def logit_link(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "reg_models = [\n",
    "    {\"model\": \"z ~ 1\", \"link_func\": identity_link},\n",
    "    {\"model\": \"v ~ 1 + cov_1 * cov_2\", \"link_func\": identity_link},\n",
    "]\n",
    "\n",
    "hddm_models_reg_interact = []\n",
    "n_models = 1\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_reg_interact.append(\n",
    "        hddm.HDDMRegressor(\n",
    "            data,\n",
    "            reg_models,\n",
    "            informative=True,\n",
    "            is_group_model=False,\n",
    "            group_only_regressors=True,\n",
    "            include=[\"v\", \"a\", \"t\", \"z\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 2000\n",
    "for i in range(1):\n",
    "    print(i)\n",
    "    hddm_models_reg_interact[i].sample(nmcmc, burn=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "811d9ca3-035c-4eff-9fb4-942ebfbf1396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.05356</td>\n",
       "      <td>0.061154</td>\n",
       "      <td>2.94053</td>\n",
       "      <td>3.013907</td>\n",
       "      <td>3.053218</td>\n",
       "      <td>3.094195</td>\n",
       "      <td>3.174831</td>\n",
       "      <td>0.003217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.979239</td>\n",
       "      <td>0.015832</td>\n",
       "      <td>0.947269</td>\n",
       "      <td>0.967998</td>\n",
       "      <td>0.980263</td>\n",
       "      <td>0.990506</td>\n",
       "      <td>1.008643</td>\n",
       "      <td>0.00079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_Intercept</th>\n",
       "      <td>0.385072</td>\n",
       "      <td>0.010805</td>\n",
       "      <td>0.36276</td>\n",
       "      <td>0.378506</td>\n",
       "      <td>0.385366</td>\n",
       "      <td>0.392151</td>\n",
       "      <td>0.40637</td>\n",
       "      <td>0.000472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept</th>\n",
       "      <td>0.992757</td>\n",
       "      <td>0.036641</td>\n",
       "      <td>0.921336</td>\n",
       "      <td>0.96781</td>\n",
       "      <td>0.992819</td>\n",
       "      <td>1.01786</td>\n",
       "      <td>1.06565</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_cov_1</th>\n",
       "      <td>0.965131</td>\n",
       "      <td>0.048614</td>\n",
       "      <td>0.868896</td>\n",
       "      <td>0.932833</td>\n",
       "      <td>0.965775</td>\n",
       "      <td>1.00036</td>\n",
       "      <td>1.055826</td>\n",
       "      <td>0.002069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_cov_2</th>\n",
       "      <td>-0.982753</td>\n",
       "      <td>0.050947</td>\n",
       "      <td>-1.082958</td>\n",
       "      <td>-1.017807</td>\n",
       "      <td>-0.982696</td>\n",
       "      <td>-0.947519</td>\n",
       "      <td>-0.88273</td>\n",
       "      <td>0.002077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_cov_1:cov_2</th>\n",
       "      <td>0.459941</td>\n",
       "      <td>0.091786</td>\n",
       "      <td>0.280086</td>\n",
       "      <td>0.399174</td>\n",
       "      <td>0.46033</td>\n",
       "      <td>0.52191</td>\n",
       "      <td>0.635334</td>\n",
       "      <td>0.002942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean       std      2.5q       25q       50q       75q  \\\n",
       "a               3.05356  0.061154   2.94053  3.013907  3.053218  3.094195   \n",
       "t              0.979239  0.015832  0.947269  0.967998  0.980263  0.990506   \n",
       "z_Intercept    0.385072  0.010805   0.36276  0.378506  0.385366  0.392151   \n",
       "v_Intercept    0.992757  0.036641  0.921336   0.96781  0.992819   1.01786   \n",
       "v_cov_1        0.965131  0.048614  0.868896  0.932833  0.965775   1.00036   \n",
       "v_cov_2       -0.982753  0.050947 -1.082958 -1.017807 -0.982696 -0.947519   \n",
       "v_cov_1:cov_2  0.459941  0.091786  0.280086  0.399174   0.46033   0.52191   \n",
       "\n",
       "                  97.5q    mc err  \n",
       "a              3.174831  0.003217  \n",
       "t              1.008643   0.00079  \n",
       "z_Intercept     0.40637  0.000472  \n",
       "v_Intercept     1.06565  0.001879  \n",
       "v_cov_1        1.055826  0.002069  \n",
       "v_cov_2        -0.88273  0.002077  \n",
       "v_cov_1:cov_2  0.635334  0.002942  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_reg_interact[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8818e7c-38c8-4ff0-ab87-062773b8e2cc",
   "metadata": {},
   "source": [
    "##### NOTES:\n",
    "\n",
    "A note on the interpretation of the **beta coefficients**. \n",
    "Let's say we as whether our interaction term is \"*significantly*\" different from 0.\n",
    "\n",
    "We can plot the trace and signify e.g. it's $5th$ percentile. \n",
    "If the $5th$ percentile is above 0 (or the $95th$ percentile below 0) we can conclude with some confidence that \n",
    "there is a **positive interaction** (negative interaction) between *cov_1* and *cov_2*.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "9e81e1db-18ac-4585-8fe3-c2a3f18de0ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bayesian 'hypothesis test' checking whether the interaction is 'significant'\n",
    "\n",
    "interaction_trace = hddm_models_reg_interact[0].get_traces()[\"v_cov_1:cov_2\"].values\n",
    "interaction_trace_sorted = np.sort(interaction_trace)\n",
    "\n",
    "percentile = 0.05\n",
    "plt.hist(interaction_trace, bins=30, histtype=\"step\", density=True)\n",
    "plt.axvline(\n",
    "    x=interaction_trace_sorted[int(np.floor(percentile * interaction_trace.shape[0]))],\n",
    "    color=\"red\",\n",
    "    linestyle=\"dashed\",\n",
    ")\n",
    "plt.title(\"Interaction beta posteiror\")\n",
    "plt.xlim((0, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a912c6cd-ce9d-4565-a780-bcff414882ab",
   "metadata": {},
   "source": [
    "### Interactions (Categorical Covariates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d49844-d953-4c4b-be72-2c3557eda98f",
   "metadata": {},
   "source": [
    "This example comes closest to **ANOVA** style analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "601af10a-5193-4830-97cf-85b7966662a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make data\n",
    "n_trials = 1000\n",
    "\n",
    "beta_v_intercept = 1\n",
    "beta_v_cov_1 = 1\n",
    "beta_v_cov_2 = -1\n",
    "beta_v_interaction = 0.5\n",
    "cov_1 = np.concatenate(\n",
    "    [np.ones(500), np.zeros(500)]\n",
    ")  # np.random.uniform(low = -1, high = 1, size = n_trials)\n",
    "cov_2 = np.concatenate(\n",
    "    [np.ones(250), np.zeros(250), np.ones(250), np.zeros(250)]\n",
    ")  # np.random.uniform(low = -1, high = 1, size = n_trials)\n",
    "v = (\n",
    "    beta_v_intercept\n",
    "    + beta_v_cov_1 * cov_1\n",
    "    + beta_v_cov_2 * cov_2\n",
    "    + beta_v_interaction * cov_1 * cov_2\n",
    ")\n",
    "a = 3\n",
    "t = 1\n",
    "z = 0.4\n",
    "\n",
    "theta = np.zeros((n_trials, 4))\n",
    "theta[:, 0] = v\n",
    "theta[:, 1] = a\n",
    "theta[:, 2] = z\n",
    "theta[:, 3] = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "2496d9ff-419a-4ef9-827f-53d8c7360bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_out = hddm.simulators.simulator(\n",
    "    theta=theta, model=\"ddm_hddm_base\", n_samples=1, max_t=40\n",
    ")\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.hstack([sim_out[0], sim_out[1], theta]),\n",
    "    columns=[\"rt\", \"response\", \"v\", \"a\", \"z\", \"t\"],\n",
    ")\n",
    "data[\"cov_1\"] = cov_1\n",
    "data[\"cov_2\"] = cov_2\n",
    "data[\"cov_1\"] = data[\"cov_1\"].apply(str)\n",
    "data[\"cov_2\"] = data[\"cov_2\"].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "6c5d7189-fb60-4931-b605-7ad0d3f69488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>v</th>\n",
       "      <th>a</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "      <th>cov_1</th>\n",
       "      <th>cov_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.806993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.618020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.493997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.094995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.716994</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>3.139027</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>3.453005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1.460998</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>3.128028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1.389999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           rt  response    v    a    z    t cov_1 cov_2\n",
       "0    1.806993       1.0  1.5  3.0  0.4  1.0   1.0   1.0\n",
       "1    2.618020       1.0  1.5  3.0  0.4  1.0   1.0   1.0\n",
       "2    1.493997       1.0  1.5  3.0  0.4  1.0   1.0   1.0\n",
       "3    2.094995       1.0  1.5  3.0  0.4  1.0   1.0   1.0\n",
       "4    1.716994       1.0  1.5  3.0  0.4  1.0   1.0   1.0\n",
       "..        ...       ...  ...  ...  ...  ...   ...   ...\n",
       "995  3.139027       1.0  1.0  3.0  0.4  1.0   0.0   0.0\n",
       "996  3.453005       1.0  1.0  3.0  0.4  1.0   0.0   0.0\n",
       "997  1.460998       1.0  1.0  3.0  0.4  1.0   0.0   0.0\n",
       "998  3.128028       1.0  1.0  3.0  0.4  1.0   0.0   0.0\n",
       "999  1.389999       1.0  1.0  3.0  0.4  1.0   0.0   0.0\n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "a6cc874a-9206-4c13-8fe2-73dd0f2dfe64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      "0\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 122.6 sec"
     ]
    }
   ],
   "source": [
    "# Run Model\n",
    "def identity_link(x):\n",
    "    return x\n",
    "\n",
    "\n",
    "def logit_link(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "reg_models = [\n",
    "    {\"model\": \"z ~ 1\", \"link_func\": identity_link},\n",
    "    {\"model\": \"v ~ 1 + cov_1 * cov_2\", \"link_func\": identity_link},\n",
    "]\n",
    "\n",
    "hddm_models_reg_interact = []\n",
    "n_models = 1\n",
    "\n",
    "for i in range(n_models):\n",
    "    hddm_models_reg_interact.append(\n",
    "        hddm.HDDMRegressor(\n",
    "            data,\n",
    "            reg_models,\n",
    "            informative=True,\n",
    "            is_group_model=False,\n",
    "            group_only_regressors=True,\n",
    "            include=[\"v\", \"a\", \"t\", \"z\"],\n",
    "            p_outlier=0,\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Sample\n",
    "nmcmc = 2000\n",
    "for i in range(1):\n",
    "    print(i)\n",
    "    hddm_models_reg_interact[i].sample(nmcmc, burn=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "76628074-4f2f-42a8-b5dc-7d2f417d59bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>2.5q</th>\n",
       "      <th>25q</th>\n",
       "      <th>50q</th>\n",
       "      <th>75q</th>\n",
       "      <th>97.5q</th>\n",
       "      <th>mc err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.133511</td>\n",
       "      <td>0.065667</td>\n",
       "      <td>3.00346</td>\n",
       "      <td>3.08932</td>\n",
       "      <td>3.132351</td>\n",
       "      <td>3.180116</td>\n",
       "      <td>3.259844</td>\n",
       "      <td>0.003913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.991305</td>\n",
       "      <td>0.017442</td>\n",
       "      <td>0.954532</td>\n",
       "      <td>0.98048</td>\n",
       "      <td>0.991255</td>\n",
       "      <td>1.003074</td>\n",
       "      <td>1.025361</td>\n",
       "      <td>0.001076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_Intercept</th>\n",
       "      <td>0.41207</td>\n",
       "      <td>0.012066</td>\n",
       "      <td>0.388513</td>\n",
       "      <td>0.40434</td>\n",
       "      <td>0.412275</td>\n",
       "      <td>0.419599</td>\n",
       "      <td>0.43572</td>\n",
       "      <td>0.000716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_Intercept</th>\n",
       "      <td>1.090374</td>\n",
       "      <td>0.052223</td>\n",
       "      <td>0.994351</td>\n",
       "      <td>1.054342</td>\n",
       "      <td>1.090284</td>\n",
       "      <td>1.126844</td>\n",
       "      <td>1.192983</td>\n",
       "      <td>0.003714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_cov_1[T.1.0]</th>\n",
       "      <td>0.940493</td>\n",
       "      <td>0.082819</td>\n",
       "      <td>0.783328</td>\n",
       "      <td>0.883556</td>\n",
       "      <td>0.942071</td>\n",
       "      <td>0.992947</td>\n",
       "      <td>1.112397</td>\n",
       "      <td>0.005801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_cov_2[T.1.0]</th>\n",
       "      <td>-1.090818</td>\n",
       "      <td>0.062185</td>\n",
       "      <td>-1.218202</td>\n",
       "      <td>-1.132491</td>\n",
       "      <td>-1.087307</td>\n",
       "      <td>-1.047429</td>\n",
       "      <td>-0.974007</td>\n",
       "      <td>0.004176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_cov_1[T.1.0]:cov_2[T.1.0]</th>\n",
       "      <td>0.650929</td>\n",
       "      <td>0.109233</td>\n",
       "      <td>0.436134</td>\n",
       "      <td>0.577175</td>\n",
       "      <td>0.649197</td>\n",
       "      <td>0.728338</td>\n",
       "      <td>0.856451</td>\n",
       "      <td>0.0078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 mean       std      2.5q       25q       50q  \\\n",
       "a                            3.133511  0.065667   3.00346   3.08932  3.132351   \n",
       "t                            0.991305  0.017442  0.954532   0.98048  0.991255   \n",
       "z_Intercept                   0.41207  0.012066  0.388513   0.40434  0.412275   \n",
       "v_Intercept                  1.090374  0.052223  0.994351  1.054342  1.090284   \n",
       "v_cov_1[T.1.0]               0.940493  0.082819  0.783328  0.883556  0.942071   \n",
       "v_cov_2[T.1.0]              -1.090818  0.062185 -1.218202 -1.132491 -1.087307   \n",
       "v_cov_1[T.1.0]:cov_2[T.1.0]  0.650929  0.109233  0.436134  0.577175  0.649197   \n",
       "\n",
       "                                  75q     97.5q    mc err  \n",
       "a                            3.180116  3.259844  0.003913  \n",
       "t                            1.003074  1.025361  0.001076  \n",
       "z_Intercept                  0.419599   0.43572  0.000716  \n",
       "v_Intercept                  1.126844  1.192983  0.003714  \n",
       "v_cov_1[T.1.0]               0.992947  1.112397  0.005801  \n",
       "v_cov_2[T.1.0]              -1.047429 -0.974007  0.004176  \n",
       "v_cov_1[T.1.0]:cov_2[T.1.0]  0.728338  0.856451    0.0078  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm_models_reg_interact[0].gen_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ee08b5-978f-4458-b55a-ccd08f17c679",
   "metadata": {},
   "source": [
    "### Priors\n",
    "\n",
    "We try to illustrate the effect of choosing between *informative* and *uninformative* priors in your data analysis (and relatedly the added benefit you might expect from introducing more user side freedom on prior choice). We pick a number of different dataset-sizes (trial numbers), run inference *once with informative* and *once with uninformative* priors and then compare the posteriors for each of the parameters **\\[v, a, z, t\\]**.\n",
    "\n",
    "**Spoiler-alert**:\n",
    "\n",
    "Don't expect much !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "b234c86b-b391-48f0-8575-eb01fa76bb77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta is supplied as list --> simulator assumes n_trials = 1\n"
     ]
    }
   ],
   "source": [
    "# Generate some data\n",
    "n_samples = 10000\n",
    "parameters = hddm.model_config.model_config[\"ddm_hddm_base\"][\"params_default\"]\n",
    "parameters[hddm.model_config.model_config[\"ddm_hddm_base\"][\"params\"].index(\"t\")] = 0.5\n",
    "\n",
    "theta = np.zeros((n_samples, 4))\n",
    "theta[:, 0] = parameters[\n",
    "    hddm.model_config.model_config[\"ddm_hddm_base\"][\"params\"].index(\"v\")\n",
    "]\n",
    "theta[:, 1] = parameters[\n",
    "    hddm.model_config.model_config[\"ddm_hddm_base\"][\"params\"].index(\"a\")\n",
    "]\n",
    "theta[:, 2] = parameters[\n",
    "    hddm.model_config.model_config[\"ddm_hddm_base\"][\"params\"].index(\"z\")\n",
    "]\n",
    "theta[:, 3] = parameters[\n",
    "    hddm.model_config.model_config[\"ddm_hddm_base\"][\"params\"].index(\"t\")\n",
    "]\n",
    "\n",
    "sim_out = hddm.simulators.simulator(\n",
    "    theta=parameters, model=\"ddm_hddm_base\", n_samples=n_samples, max_t=40\n",
    ")\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.hstack([sim_out[0], sim_out[1], theta]),\n",
    "    columns=[\"rt\", \"response\", \"v\", \"a\", \"z\", \"t\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "dadd96e0-f6a3-4598-bb7a-1c4d3a10c048",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size:  32\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 7.5 secNo model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 7.4 secSample size:  64\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 6.6 secNo model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 6.5 secSample size:  128\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 6.4 secNo model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 6.4 secSample size:  256\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 7.2 secNo model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 7.2 secSample size:  512\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 9.7 secNo model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 9.7 secSample size:  1024\n",
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 14.2 secNo model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 1000 of 1000 complete in 14.3 sec"
     ]
    }
   ],
   "source": [
    "# Choose sample sizes for our model fits\n",
    "sample_sizes = [32, 64, 128, 256, 512, 1024]\n",
    "stats_list = []\n",
    "trace_list = []\n",
    "\n",
    "for n_samples_tmp in sample_sizes:\n",
    "    print(\"Sample size: \", n_samples_tmp)\n",
    "    data_tmp = data.sample(n_samples_tmp).reset_index(drop=True)\n",
    "\n",
    "    # Informative -----\n",
    "    hddm_model_tmp = hddm.HDDM(\n",
    "        data_tmp,\n",
    "        informative=True,\n",
    "        is_group_model=False,\n",
    "        include=[\"v\", \"a\", \"t\", \"z\"],\n",
    "        p_outlier=0,\n",
    "    )\n",
    "    hddm_model_tmp.sample(1000, burn=500)\n",
    "\n",
    "    # Save traces\n",
    "    tmp_traces = hddm_model_tmp.get_traces()\n",
    "    tmp_traces[\"informative\"] = 1\n",
    "    tmp_traces[\"sample_size\"] = n_samples_tmp\n",
    "    trace_list.append(deepcopy(tmp_traces))\n",
    "    # -----\n",
    "\n",
    "    # Uninformative -----\n",
    "    hddm_model_tmp = hddm.HDDM(\n",
    "        data_tmp,\n",
    "        informative=False,\n",
    "        is_group_model=False,\n",
    "        include=[\"v\", \"a\", \"t\", \"z\"],\n",
    "        p_outlier=0,\n",
    "    )\n",
    "    hddm_model_tmp.sample(1000, burn=500)\n",
    "\n",
    "    # Save traces\n",
    "    tmp_traces = hddm_model_tmp.get_traces()\n",
    "    tmp_traces[\"informative\"] = 0\n",
    "    tmp_traces[\"sample_size\"] = n_samples_tmp\n",
    "    trace_list.append(deepcopy(tmp_traces))\n",
    "    # -----\n",
    "\n",
    "trace_df = pd.concat(trace_list)\n",
    "trace_df[\"sample_size\"] = trace_df[\"sample_size\"].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "4a33f0b2-cd62-49a7-b287-6c643b8ab356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 437.925x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 437.925x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 437.925x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 437.925x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for param in [\"v\", \"a\", \"z_trans\", \"t\"]:\n",
    "    g = sns.catplot(\n",
    "        x=param,\n",
    "        y=\"sample_size\",\n",
    "        hue=\"informative\",\n",
    "        kind=\"violin\",\n",
    "        data=trace_df,\n",
    "        title=param,\n",
    "    )\n",
    "    g.fig.subplots_adjust(top=0.9)\n",
    "    g.fig.suptitle(param)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2965a0-e401-497a-845a-a0efcc272777",
   "metadata": {},
   "source": [
    "### END"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hddmnn_tutorial",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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